USDA Inflammation Metabolomics Data analysis
Urine, C18(-) LCMS
Maria Sholola
2023-06-05
Load libraries
library(tidyverse) # for everything
library(readxl) # for reading in excel files
library(janitor) # data checks and cleaning
library(glue) # for easy pasting
library(FactoMineR) # for PCA
library(factoextra) # for PCA
library(rstatix) # for stats
library(pheatmap) # for heatmaps
library(plotly) # for interactive plots
library(htmlwidgets) # for saving interactive plots
library(devtools)
library(notame) # used for feature clustering
library(doParallel)
library(igraph) # feature clustering
library(ggpubr) # visualizations
library(knitr) # clean table printing
library(rmarkdown)
library(mixOmics) # for multilevel PCAs
library(corrr)
library(ggcorrplot)
library(ggthemes)
library(ggtext)
library(PCAtools)
library(pathview) # for functional analysis and KEGG annotationRead in data
Wrangle data
# rename "row ID"
omicsdata <- omicsdata %>%
rename("row_ID" = `row ID`)
# how many features
nrow(omicsdata)## [1] 1411## # A tibble: 4 × 59
## mz_rt dupe_count row_ID `6111_U4_C18NEG_42` `6101_U2_C18NEG_30`
## <chr> <int> <dbl> <dbl> <dbl>
## 1 361.0666_4.291 2 7185 NA NA
## 2 361.0666_4.291 2 7192 NA NA
## 3 385.1685_4.355 2 7299 27486. 5135.
## 4 385.1685_4.355 2 7306 27486. 5135.
## # ℹ 54 more variables: `6102_U1_C18NEG_26` <dbl>, `6110_U2_C18NEG_34` <dbl>,
## # `6105_U4_C18NEG_47` <dbl>, `6103_U4_C18NEG_53` <dbl>,
## # `6105_U1_C18NEG_43` <dbl>, `6104_U1_C18NEG_55` <dbl>,
## # `6113_U4_C18NEG_62` <dbl>, `6105_U2_C18NEG_40` <dbl>,
## # `6113_U3_C18NEG_15` <dbl>, `6102_U3_C18NEG_48` <dbl>,
## # `6101_U3_C18NEG_29` <dbl>, `6108_U4_C18NEG_18` <dbl>,
## # `6104_U4_C18NEG_12` <dbl>, `6111_U1_C18NEG_51` <dbl>, …# remove dupes
omicsdata <- omicsdata %>%
distinct(mz_rt, .keep_all = TRUE)
# check again for dupes
omicsdata %>% get_dupes(mz_rt)## # A tibble: 0 × 59
## # ℹ 59 variables: mz_rt <chr>, dupe_count <int>, row_ID <dbl>,
## # 6111_U4_C18NEG_42 <dbl>, 6101_U2_C18NEG_30 <dbl>, 6102_U1_C18NEG_26 <dbl>,
## # 6110_U2_C18NEG_34 <dbl>, 6105_U4_C18NEG_47 <dbl>, 6103_U4_C18NEG_53 <dbl>,
## # 6105_U1_C18NEG_43 <dbl>, 6104_U1_C18NEG_55 <dbl>, 6113_U4_C18NEG_62 <dbl>,
## # 6105_U2_C18NEG_40 <dbl>, 6113_U3_C18NEG_15 <dbl>, 6102_U3_C18NEG_48 <dbl>,
## # 6101_U3_C18NEG_29 <dbl>, 6108_U4_C18NEG_18 <dbl>, 6104_U4_C18NEG_12 <dbl>,
## # 6111_U1_C18NEG_51 <dbl>, 6105_U3_C18NEG_39 <dbl>, …## [1] 1409Sometimes a weird logical column (lgl) comes up in my data. Let’s check if it’s there
## [1] "mz_rt" "row_ID"
## [3] "6111_U4_C18NEG_42" "6101_U2_C18NEG_30"
## [5] "6102_U1_C18NEG_26" "6110_U2_C18NEG_34"
## [7] "6105_U4_C18NEG_47" "6103_U4_C18NEG_53"
## [9] "6105_U1_C18NEG_43" "6104_U1_C18NEG_55"
## [11] "6113_U4_C18NEG_62" "6105_U2_C18NEG_40"
## [13] "6113_U3_C18NEG_15" "6102_U3_C18NEG_48"
## [15] "6101_U3_C18NEG_29" "6108_U4_C18NEG_18"
## [17] "6104_U4_C18NEG_12" "6111_U1_C18NEG_51"
## [19] "6105_U3_C18NEG_39" "6110_U4_C18NEG_10"
## [21] "6111_U3_C18NEG_54" "6104_U2_C18NEG_19"
## [23] "6101_U1_C18NEG_59" "6111_U2_C18NEG_35"
## [25] "6113_U2_C18NEG_31" "6101_U4_C18NEG_14"
## [27] "6103_U3_C18NEG_61" "6108_U3_C18NEG_28"
## [29] "6109_U1_C18NEG_58" "6104_U3_C18NEG_32"
## [31] "6110_U3_C18NEG_45" "6108_U2_C18NEG_27"
## [33] "6108_U1_C18NEG_44" "6102_U4_C18NEG_50"
## [35] "PooledQC10_C18NEG_64" "6103_U2_C18NEG_60"
## [37] "PooledQC9_C18NEG_57" "6102_U2_C18NEG_16"
## [39] "6112_U2_C18NEG_37" "6110_U1_C18NEG_11"
## [41] "6112_U4_C18NEG_63" "6109_U3_C18NEG_52"
## [43] "6109_U4_C18NEG_22" "6109_U2_C18NEG_13"
## [45] "6106_U4_C18NEG_36" "6113_U1_C18NEG_24"
## [47] "PooledQC5_C18NEG_25" "6106_U3_C18NEG_20"
## [49] "PooledQC6_C18NEG_33" "6112_U3_C18NEG_56"
## [51] "PooledQC4_C18NEG_17" "PooledQC8_C18NEG_49"
## [53] "PooledQC7_C18NEG_41" "6103_U1_C18NEG_21"
## [55] "6106_U1_C18NEG_23" "6106_U2_C18NEG_46"
## [57] "6112_U1_C18NEG_38" "Prerun_PooledQC3_C18NEG_7_1"# remove weird lgl column
omicsdata <- omicsdata %>%
dplyr::select(!where(is.logical))
colnames(omicsdata)## [1] "mz_rt" "row_ID"
## [3] "6111_U4_C18NEG_42" "6101_U2_C18NEG_30"
## [5] "6102_U1_C18NEG_26" "6110_U2_C18NEG_34"
## [7] "6105_U4_C18NEG_47" "6103_U4_C18NEG_53"
## [9] "6105_U1_C18NEG_43" "6104_U1_C18NEG_55"
## [11] "6113_U4_C18NEG_62" "6105_U2_C18NEG_40"
## [13] "6113_U3_C18NEG_15" "6102_U3_C18NEG_48"
## [15] "6101_U3_C18NEG_29" "6108_U4_C18NEG_18"
## [17] "6104_U4_C18NEG_12" "6111_U1_C18NEG_51"
## [19] "6105_U3_C18NEG_39" "6110_U4_C18NEG_10"
## [21] "6111_U3_C18NEG_54" "6104_U2_C18NEG_19"
## [23] "6101_U1_C18NEG_59" "6111_U2_C18NEG_35"
## [25] "6113_U2_C18NEG_31" "6101_U4_C18NEG_14"
## [27] "6103_U3_C18NEG_61" "6108_U3_C18NEG_28"
## [29] "6109_U1_C18NEG_58" "6104_U3_C18NEG_32"
## [31] "6110_U3_C18NEG_45" "6108_U2_C18NEG_27"
## [33] "6108_U1_C18NEG_44" "6102_U4_C18NEG_50"
## [35] "PooledQC10_C18NEG_64" "6103_U2_C18NEG_60"
## [37] "PooledQC9_C18NEG_57" "6102_U2_C18NEG_16"
## [39] "6112_U2_C18NEG_37" "6110_U1_C18NEG_11"
## [41] "6112_U4_C18NEG_63" "6109_U3_C18NEG_52"
## [43] "6109_U4_C18NEG_22" "6109_U2_C18NEG_13"
## [45] "6106_U4_C18NEG_36" "6113_U1_C18NEG_24"
## [47] "PooledQC5_C18NEG_25" "6106_U3_C18NEG_20"
## [49] "PooledQC6_C18NEG_33" "6112_U3_C18NEG_56"
## [51] "PooledQC4_C18NEG_17" "PooledQC8_C18NEG_49"
## [53] "PooledQC7_C18NEG_41" "6103_U1_C18NEG_21"
## [55] "6106_U1_C18NEG_23" "6106_U2_C18NEG_46"
## [57] "6112_U1_C18NEG_38" "Prerun_PooledQC3_C18NEG_7_1"# create long df for omics df
omicsdata_tidy <- omicsdata %>%
pivot_longer(cols = 3:ncol(.),
names_to = "sample_ID",
values_to = "peak_height")
# combine meta and omics dfs
meta_omics <- full_join(omicsdata_tidy,
metadata,
by = c("sample_ID" = "sample_ID"))
# separate mz and rt
meta_omics_sep <- meta_omics %>%
separate(col = mz_rt,
into = c("mz", "rt"),
sep = "_")
# convert columns to correct type
meta_omics_sep$mz <- as.numeric(meta_omics_sep$mz)
meta_omics_sep$rt <- as.numeric(meta_omics_sep$rt)
meta_omics_sep$Subject <- as.character(meta_omics_sep$Subject)
meta_omics_sep$Intervention <- as.character(meta_omics_sep$Intervention)
# rearrange column order
meta_omics_sep <- meta_omics_sep %>%
dplyr::select(sample_ID, pre_post, Intervention, everything())
str(meta_omics_sep)## tibble [78,904 × 14] (S3: tbl_df/tbl/data.frame)
## $ sample_ID : chr [1:78904] "6111_U4_C18NEG_42" "6101_U2_C18NEG_30" "6102_U1_C18NEG_26" "6110_U2_C18NEG_34" ...
## $ pre_post : chr [1:78904] "post" "post" "pre" "post" ...
## $ Intervention : chr [1:78904] "Red" "Red" "Red" "Yellow" ...
## $ mz : num [1:78904] 236 236 236 236 236 ...
## $ rt : num [1:78904] 0.603 0.603 0.603 0.603 0.603 0.603 0.603 0.603 0.603 0.603 ...
## $ row_ID : num [1:78904] 342 342 342 342 342 342 342 342 342 342 ...
## $ peak_height : num [1:78904] 4148 65119 16742 16351 46171 ...
## $ Subject : chr [1:78904] "6111" "6101" "6102" "6110" ...
## $ Period : chr [1:78904] "U4" "U2" "U1" "U2" ...
## $ sequence : chr [1:78904] "Y_R" "R_Y" "R_Y" "Y_R" ...
## $ Intervention_week: num [1:78904] 14 6 2 6 14 14 2 2 14 6 ...
## $ Sex : chr [1:78904] "M" "F" "M" "M" ...
## $ Age : num [1:78904] 46 58 65 36 40 60 40 54 61 40 ...
## $ BMI : num [1:78904] 30 31.1 36.8 29.9 30.4 ...Data summaries
mass vs RT scatterplot
# plot
(plot_mzvsrt <- meta_omics_sep %>%
ggplot(aes(x = rt, y = mz)) +
geom_point() +
theme_minimal() +
labs(x = "Retention time, min",
y = "m/z, neutral",
title = "mz across RT for all features"))
NAs and imputing
NAs
# samples only (no QCs)
omicsdata_noQC <- omicsdata %>%
dplyr::select(-contains("QC"))
#NAs in samples only?
NAbyRow_noQC <- rowSums(is.na(omicsdata_noQC[,-1]))
hist(NAbyRow_noQC,
breaks = 48, # because there are 48 samples
xlab = "Number of missing values",
ylab = "Number of metabolites",
main = "How many missing values are there?")
Are there any missing values in QCs? There shouldn’t be after data preprocessing/filtering
omicsdata_QC <- omicsdata %>%
dplyr::select(starts_with("P"))
NAbyRow_QC <- colSums(is.na(omicsdata_QC))
# lets confirm that there are no missing values from my QCs
sum(NAbyRow_QC) # no## [1] 0# calculate how many NAs there are per feature in whole data set
contains_NAs <- meta_omics %>%
group_by(mz_rt) %>%
count(is.na(peak_height)) %>%
filter(`is.na(peak_height)` == TRUE)
kable(contains_NAs)| mz_rt | is.na(peak_height) | n |
|---|---|---|
| 1003.2655_3.721 | TRUE | 44 |
| 1003.2656_3.716 | TRUE | 44 |
| 1004.2068_3.286 | TRUE | 43 |
| 1041.2898_4.199 | TRUE | 43 |
| 1069.2473_3.702 | TRUE | 42 |
| 108.0216_3.832 | TRUE | 40 |
| 108.0218_4.217 | TRUE | 41 |
| 109.0288_3.757 | TRUE | 43 |
| 109.0294_3.11 | TRUE | 24 |
| 1115.3646_3.466 | TRUE | 1 |
| 1117.2561_3.018 | TRUE | 44 |
| 1121.3811_3.466 | TRUE | 1 |
| 1122.0244_4.55 | TRUE | 14 |
| 1122.0248_4.125 | TRUE | 20 |
| 1122.0249_4.032 | TRUE | 21 |
| 1123.2723_0.775 | TRUE | 40 |
| 1123.2728_3.021 | TRUE | 44 |
| 1124.0041_3.197 | TRUE | 1 |
| 1131.2716_3.026 | TRUE | 44 |
| 1132.0633_4.207 | TRUE | 42 |
| 1135.2278_4.215 | TRUE | 42 |
| 1137.288_3.026 | TRUE | 44 |
| 1141.2436_3.619 | TRUE | 43 |
| 1141.2441_4.201 | TRUE | 42 |
| 1153.2827_0.789 | TRUE | 42 |
| 1159.3041_3.727 | TRUE | 44 |
| 1165.3189_3.729 | TRUE | 44 |
| 123.0452_3.87 | TRUE | 3 |
| 123.0452_4.211 | TRUE | 5 |
| 124.01_0.775 | TRUE | 5 |
| 124.0486_3.843 | TRUE | 43 |
| 1257.2949_3.465 | TRUE | 3 |
| 1279.2478_3.734 | TRUE | 44 |
| 1303.2955_3.702 | TRUE | 44 |
| 139.0401_0.786 | TRUE | 6 |
| 139.0401_3.003 | TRUE | 14 |
| 150.0562_3.542 | TRUE | 6 |
| 161.9866_2.133 | TRUE | 20 |
| 161.988_0.685 | TRUE | 7 |
| 167.0349_3.921 | TRUE | 1 |
| 171.0487_4.824 | TRUE | 37 |
| 175.0975_4.743 | TRUE | 4 |
| 177.1283_5.202 | TRUE | 38 |
| 181.0503_4.495 | TRUE | 21 |
| 181.0507_4.062 | TRUE | 9 |
| 181.9916_0.803 | TRUE | 4 |
| 181.9917_2.823 | TRUE | 9 |
| 182.0076_0.788 | TRUE | 9 |
| 182.0282_0.77 | TRUE | 15 |
| 187.0383_3.122 | TRUE | 2 |
| 191.1076_5.282 | TRUE | 14 |
| 191.1105_5.283 | TRUE | 16 |
| 193.0354_6.242 | TRUE | 4 |
| 197.0453_3.76 | TRUE | 21 |
| 197.0471_3.745 | TRUE | 24 |
| 203.0172_4.555 | TRUE | 4 |
| 209.0452_3.159 | TRUE | 27 |
| 209.0452_3.24 | TRUE | 29 |
| 209.0455_3.749 | TRUE | 5 |
| 209.0532_3.773 | TRUE | 3 |
| 210.0419_3.959 | TRUE | 40 |
| 211.0971_3.846 | TRUE | 5 |
| 212.0065_4.503 | TRUE | 24 |
| 212.0937_3.815 | TRUE | 2 |
| 212.0968_4.21 | TRUE | 42 |
| 216.0277_4.034 | TRUE | 2 |
| 217.0161_1.159 | TRUE | 3 |
| 218.0821_5.22 | TRUE | 1 |
| 219.0509_2.854 | TRUE | 5 |
| 219.0524_2.85 | TRUE | 11 |
| 220.0979_4.733 | TRUE | 4 |
| 221.1178_5.185 | TRUE | 28 |
| 222.0406_3.686 | TRUE | 1 |
| 223.0583_2.562 | TRUE | 32 |
| 227.0551_2.986 | TRUE | 2 |
| 227.0554_2.1 | TRUE | 29 |
| 227.0628_2.987 | TRUE | 23 |
| 227.1036_0.798 | TRUE | 4 |
| 232.0047_2.518 | TRUE | 7 |
| 236.1018_5.033 | TRUE | 19 |
| 237.0356_3.369 | TRUE | 14 |
| 237.0359_3.933 | TRUE | 16 |
| 237.1243_4.614 | TRUE | 2 |
| 239.1144_0.622 | TRUE | 1 |
| 246.0109_2.385 | TRUE | 1 |
| 247.0798_4.12 | TRUE | 2 |
| 251.0128_3.524 | TRUE | 2 |
| 252.0891_4.335 | TRUE | 41 |
| 253.0503_5.048 | TRUE | 28 |
| 254.6229_6.168 | TRUE | 1 |
| 255.0674_5.078 | TRUE | 21 |
| 257.0801_5.284 | TRUE | 14 |
| 257.0816_6.056 | TRUE | 31 |
| 258.044_2.007 | TRUE | 13 |
| 260.0239_2.686 | TRUE | 5 |
| 263.0591_2.805 | TRUE | 2 |
| 263.1054_2.91 | TRUE | 1 |
| 265.0184_2.828 | TRUE | 9 |
| 269.0163_3.668 | TRUE | 16 |
| 276.0009_3.151 | TRUE | 34 |
| 277.0353_3.723 | TRUE | 1 |
| 277.0852_0.658 | TRUE | 4 |
| 279.0487_3.569 | TRUE | 12 |
| 279.0491_3.95 | TRUE | 1 |
| 279.049_3.659 | TRUE | 7 |
| 279.0984_3.799 | TRUE | 1 |
| 280.0779_6.362 | TRUE | 44 |
| 293.0743_2.543 | TRUE | 5 |
| 294.149_5.025 | TRUE | 41 |
| 295.0252_3.897 | TRUE | 3 |
| 295.03_2.565 | TRUE | 15 |
| 297.1062_4.504 | TRUE | 17 |
| 305.0561_4.737 | TRUE | 13 |
| 307.0111_2.574 | TRUE | 14 |
| 307.1184_6.294 | TRUE | 1 |
| 309.0242_2.522 | TRUE | 5 |
| 310.9761_0.719 | TRUE | 2 |
| 313.0569_3.13 | TRUE | 13 |
| 320.1087_2.579 | TRUE | 44 |
| 322.1104_2.943 | TRUE | 32 |
| 322.1105_3.605 | TRUE | 37 |
| 323.0414_2.68 | TRUE | 26 |
| 324.0682_6.363 | TRUE | 43 |
| 325.0323_0.804 | TRUE | 2 |
| 326.0892_2.313 | TRUE | 4 |
| 326.0973_2.32 | TRUE | 11 |
| 328.9944_2.584 | TRUE | 25 |
| 331.1218_3.932 | TRUE | 5 |
| 334.0517_3.708 | TRUE | 43 |
| 335.0215_3.886 | TRUE | 6 |
| 336.0706_3.376 | TRUE | 13 |
| 336.1218_3.046 | TRUE | 3 |
| 336.1323_3.647 | TRUE | 24 |
| 337.0436_0.684 | TRUE | 1 |
| 337.1446_4.025 | TRUE | 3 |
| 337.1469_3.591 | TRUE | 15 |
| 338.0632_3.433 | TRUE | 37 |
| 338.0879_3.557 | TRUE | 1 |
| 338.1134_0.651 | TRUE | 8 |
| 338.1178_2.307 | TRUE | 46 |
| 341.1234_4.519 | TRUE | 1 |
| 345.1933_4.201 | TRUE | 1 |
| 347.0076_0.814 | TRUE | 2 |
| 347.0774_4.782 | TRUE | 22 |
| 347.0881_3.328 | TRUE | 13 |
| 347.17_3.394 | TRUE | 1 |
| 351.1236_4.04 | TRUE | 9 |
| 354.1007_4.717 | TRUE | 3 |
| 354.1009_5.03 | TRUE | 1 |
| 356.1002_2.592 | TRUE | 8 |
| 357.0825_3.399 | TRUE | 3 |
| 359.0958_2.977 | TRUE | 3 |
| 359.0975_2.524 | TRUE | 4 |
| 360.1388_3.006 | TRUE | 4 |
| 361.0218_2.595 | TRUE | 2 |
| 361.0646_4.442 | TRUE | 14 |
| 361.0666_4.291 | TRUE | 19 |
| 364.0955_2.559 | TRUE | 41 |
| 364.0958_3.594 | TRUE | 40 |
| 364.0972_3.027 | TRUE | 42 |
| 366.0851_4.55 | TRUE | 16 |
| 368.0971_3.297 | TRUE | 9 |
| 368.9723_3.004 | TRUE | 2 |
| 372.1116_3.914 | TRUE | 2 |
| 373.0764_3.153 | TRUE | 6 |
| 373.1513_3.174 | TRUE | 15 |
| 373.1514_3.715 | TRUE | 1 |
| 374.1378_5.008 | TRUE | 40 |
| 374.1587_3.706 | TRUE | 34 |
| 375.0931_2.077 | TRUE | 44 |
| 377.1019_5.09 | TRUE | 12 |
| 379.1393_5.891 | TRUE | 3 |
| 379.1955_5.602 | TRUE | 12 |
| 381.0802_3.385 | TRUE | 1 |
| 381.1551_6.237 | TRUE | 4 |
| 381.1937_6.247 | TRUE | 2 |
| 387.1489_5.77 | TRUE | 27 |
| 389.1005_2.94 | TRUE | 1 |
| 389.1043_2.952 | TRUE | 3 |
| 389.1059_2.672 | TRUE | 11 |
| 391.1122_2.774 | TRUE | 16 |
| 395.1144_4.677 | TRUE | 20 |
| 396.1124_4.459 | TRUE | 1 |
| 396.1279_4.011 | TRUE | 10 |
| 396.1297_3.506 | TRUE | 26 |
| 397.1895_4.429 | TRUE | 9 |
| 397.1911_4.067 | TRUE | 29 |
| 401.1094_4.24 | TRUE | 4 |
| 403.066_3.865 | TRUE | 7 |
| 404.0395_3.379 | TRUE | 2 |
| 404.0405_3.511 | TRUE | 1 |
| 405.0285_1.16 | TRUE | 2 |
| 405.1707_5.449 | TRUE | 6 |
| 405.2644_6.291 | TRUE | 5 |
| 407.0405_4.991 | TRUE | 2 |
| 407.129_5.446 | TRUE | 16 |
| 407.2799_5.411 | TRUE | 1 |
| 407.2799_5.805 | TRUE | 2 |
| 407.2801_6.791 | TRUE | 6 |
| 411.0787_0.764 | TRUE | 1 |
| 411.0792_3.55 | TRUE | 1 |
| 411.0814_2.735 | TRUE | 2 |
| 412.1044_3.523 | TRUE | 1 |
| 413.1436_3.2 | TRUE | 3 |
| 415.0625_4.788 | TRUE | 28 |
| 416.1536_5.683 | TRUE | 4 |
| 417.0951_3.717 | TRUE | 7 |
| 417.1173_4.046 | TRUE | 6 |
| 417.1192_4.5 | TRUE | 8 |
| 418.1327_4.164 | TRUE | 34 |
| 418.1842_3.015 | TRUE | 33 |
| 419.1155_2.861 | TRUE | 28 |
| 421.0582_5.463 | TRUE | 11 |
| 422.0544_2.559 | TRUE | 29 |
| 422.1067_0.783 | TRUE | 4 |
| 422.1084_2.915 | TRUE | 38 |
| 425.0363_4.553 | TRUE | 1 |
| 427.0103_1.161 | TRUE | 2 |
| 427.0844_2.991 | TRUE | 12 |
| 428.0941_2.98 | TRUE | 14 |
| 428.0955_4.037 | TRUE | 31 |
| 428.0976_3.601 | TRUE | 31 |
| 428.9933_3.184 | TRUE | 1 |
| 429.0565_4.442 | TRUE | 18 |
| 429.0571_4.306 | TRUE | 19 |
| 429.0815_4.032 | TRUE | 4 |
| 429.082_3.756 | TRUE | 6 |
| 429.0831_3.189 | TRUE | 13 |
| 429.083_3.201 | TRUE | 14 |
| 431.0979_3.907 | TRUE | 8 |
| 431.1141_3.739 | TRUE | 18 |
| 431.1146_4.185 | TRUE | 2 |
| 432.0832_2.56 | TRUE | 44 |
| 432.1213_3.706 | TRUE | 30 |
| 432.1447_3.927 | TRUE | 22 |
| 432.1631_3.52 | TRUE | 34 |
| 432.1639_2.932 | TRUE | 39 |
| 432.2024_0.669 | TRUE | 43 |
| 432.2027_3.06 | TRUE | 42 |
| 433.0646_4.045 | TRUE | 7 |
| 433.113_4.886 | TRUE | 8 |
| 434.1396_3.922 | TRUE | 24 |
| 436.092_0.669 | TRUE | 1 |
| 436.1064_5.004 | TRUE | 39 |
| 437.0526_5.832 | TRUE | 6 |
| 437.18_7.078 | TRUE | 14 |
| 439.1146_6.219 | TRUE | 30 |
| 441.1026_3.212 | TRUE | 15 |
| 442.1246_5.012 | TRUE | 39 |
| 443.0945_3.705 | TRUE | 10 |
| 445.0782_4.484 | TRUE | 1 |
| 445.1072_3.893 | TRUE | 3 |
| 446.0335_2.313 | TRUE | 7 |
| 446.1142_3.879 | TRUE | 19 |
| 446.1795_4.499 | TRUE | 4 |
| 446.1831_4.341 | TRUE | 3 |
| 447.0921_4.067 | TRUE | 1 |
| 447.0928_3.777 | TRUE | 1 |
| 447.092_4.187 | TRUE | 1 |
| 448.1934_0.807 | TRUE | 22 |
| 448.1959_3.232 | TRUE | 37 |
| 448.1961_5.778 | TRUE | 42 |
| 448.1966_4.911 | TRUE | 30 |
| 449.0698_2.589 | TRUE | 12 |
| 449.1074_4.301 | TRUE | 2 |
| 449.142_6.234 | TRUE | 17 |
| 455.1062_4.407 | TRUE | 8 |
| 457.0623_2.989 | TRUE | 1 |
| 457.0651_3.354 | TRUE | 15 |
| 457.0963_2.367 | TRUE | 40 |
| 457.097_2.652 | TRUE | 24 |
| 457.2207_6.206 | TRUE | 14 |
| 458.9733_3.815 | TRUE | 5 |
| 459.0361_4.296 | TRUE | 3 |
| 459.0924_4.452 | TRUE | 6 |
| 459.0926_3.306 | TRUE | 13 |
| 459.093_3.801 | TRUE | 1 |
| 459.1629_2.778 | TRUE | 5 |
| 461.0817_4.193 | TRUE | 8 |
| 461.0857_3.898 | TRUE | 8 |
| 461.0863_3.571 | TRUE | 5 |
| 463.0831_5.418 | TRUE | 1 |
| 464.0663_2.822 | TRUE | 6 |
| 465.1366_4.417 | TRUE | 3 |
| 466.0888_4.204 | TRUE | 14 |
| 468.0438_2.896 | TRUE | 6 |
| 468.0834_2.815 | TRUE | 24 |
| 471.0822_3.708 | TRUE | 1 |
| 471.199_5.262 | TRUE | 13 |
| 474.1054_5.422 | TRUE | 22 |
| 475.2184_5.932 | TRUE | 1 |
| 476.0359_3.919 | TRUE | 2 |
| 476.0415_4.203 | TRUE | 38 |
| 476.1913_6.018 | TRUE | 40 |
| 477.1007_0.807 | TRUE | 16 |
| 477.1039_2.949 | TRUE | 14 |
| 477.1039_4.686 | TRUE | 8 |
| 479.0459_2.443 | TRUE | 16 |
| 479.0465_3.005 | TRUE | 8 |
| 479.0506_0.791 | TRUE | 2 |
| 481.0163_4.286 | TRUE | 7 |
| 482.2186_4.626 | TRUE | 32 |
| 483.0154_2.51 | TRUE | 11 |
| 484.0498_2.566 | TRUE | 9 |
| 485.0499_4.198 | TRUE | 12 |
| 486.1199_5.689 | TRUE | 41 |
| 487.0726_2.982 | TRUE | 6 |
| 487.0759_2.632 | TRUE | 28 |
| 488.0429_4.206 | TRUE | 40 |
| 489.0936_2.593 | TRUE | 8 |
| 489.2111_3.578 | TRUE | 22 |
| 489.2123_4.076 | TRUE | 16 |
| 491.1861_5.71 | TRUE | 10 |
| 491.191_4.507 | TRUE | 12 |
| 492.1871_5.243 | TRUE | 40 |
| 492.1884_4.319 | TRUE | 24 |
| 493.0642_3.179 | TRUE | 1 |
| 493.0653_3.147 | TRUE | 1 |
| 493.1858_5.275 | TRUE | 14 |
| 493.2869_6.516 | TRUE | 7 |
| 494.0742_2.602 | TRUE | 22 |
| 495.2227_4.812 | TRUE | 2 |
| 495.2229_3.594 | TRUE | 24 |
| 495.223_4.067 | TRUE | 4 |
| 496.198_6.048 | TRUE | 40 |
| 497.0695_3.785 | TRUE | 5 |
| 497.2379_4.163 | TRUE | 2 |
| 500.1902_3.066 | TRUE | 41 |
| 500.282_5.708 | TRUE | 44 |
| 501.1047_6.238 | TRUE | 30 |
| 501.1339_4.505 | TRUE | 4 |
| 501.2122_5.698 | TRUE | 10 |
| 502.1148_5.219 | TRUE | 36 |
| 502.2071_0.806 | TRUE | 43 |
| 503.2011_4.23 | TRUE | 16 |
| 505.1427_2.841 | TRUE | 6 |
| 505.2452_7.002 | TRUE | 31 |
| 507.0806_3.582 | TRUE | 24 |
| 509.0384_0.699 | TRUE | 5 |
| 509.0409_3.291 | TRUE | 3 |
| 509.1453_5.277 | TRUE | 14 |
| 511.1128_6.237 | TRUE | 32 |
| 512.1916_6.018 | TRUE | 38 |
| 513.0964_3.899 | TRUE | 2 |
| 514.136_3.993 | TRUE | 1 |
| 514.1681_4.367 | TRUE | 23 |
| 514.1698_4.543 | TRUE | 21 |
| 515.1648_5.296 | TRUE | 17 |
| 516.1022_2.528 | TRUE | 12 |
| 516.2234_0.803 | TRUE | 44 |
| 517.0977_4.399 | TRUE | 4 |
| 517.1295_6.231 | TRUE | 27 |
| 517.1331_3.892 | TRUE | 9 |
| 517.2049_4.804 | TRUE | 20 |
| 517.2413_8.045 | TRUE | 1 |
| 519.223_7.484 | TRUE | 35 |
| 521.1082_4.508 | TRUE | 2 |
| 521.239_6.221 | TRUE | 10 |
| 525.0334_3.606 | TRUE | 6 |
| 527.1086_4.425 | TRUE | 2 |
| 528.2617_5.454 | TRUE | 1 |
| 529.162_5.829 | TRUE | 13 |
| 531.1311_5.287 | TRUE | 10 |
| 531.1447_4.141 | TRUE | 4 |
| 533.1257_4.426 | TRUE | 6 |
| 535.0762_5.453 | TRUE | 32 |
| 536.1898_4.583 | TRUE | 38 |
| 536.3209_5.3 | TRUE | 41 |
| 537.1508_5.278 | TRUE | 7 |
| 539.1147_5.072 | TRUE | 7 |
| 541.1395_0.742 | TRUE | 7 |
| 543.2074_5.421 | TRUE | 14 |
| 544.1472_4.514 | TRUE | 25 |
| 544.1789_6.025 | TRUE | 35 |
| 544.2886_5.154 | TRUE | 30 |
| 547.1102_3.005 | TRUE | 16 |
| 549.035_2.814 | TRUE | 24 |
| 549.0364_0.757 | TRUE | 6 |
| 549.1948_4.514 | TRUE | 12 |
| 550.2061_4.628 | TRUE | 35 |
| 551.0331_2.81 | TRUE | 1 |
| 551.2033_6.504 | TRUE | 13 |
| 553.0631_4.891 | TRUE | 22 |
| 553.1926_5.396 | TRUE | 26 |
| 555.0787_2.996 | TRUE | 17 |
| 555.1553_0.783 | TRUE | 25 |
| 556.1624_3.635 | TRUE | 32 |
| 557.1344_0.671 | TRUE | 1 |
| 557.2465_7.479 | TRUE | 32 |
| 558.136_3.478 | TRUE | 1 |
| 559.0435_3.584 | TRUE | 15 |
| 559.0453_4.234 | TRUE | 17 |
| 560.1364_2.889 | TRUE | 11 |
| 560.1365_2.971 | TRUE | 25 |
| 561.231_6.512 | TRUE | 8 |
| 562.1922_4.035 | TRUE | 17 |
| 565.0065_2.811 | TRUE | 19 |
| 565.0578_3.576 | TRUE | 11 |
| 565.0619_4.188 | TRUE | 2 |
| 565.137_2.579 | TRUE | 36 |
| 565.1563_4.799 | TRUE | 1 |
| 569.0928_2.988 | TRUE | 6 |
| 569.0956_3.38 | TRUE | 1 |
| 569.1299_4.178 | TRUE | 4 |
| 569.1307_3.754 | TRUE | 2 |
| 569.1751_3.441 | TRUE | 1 |
| 571.1469_3.618 | TRUE | 4 |
| 571.1476_4.081 | TRUE | 12 |
| 572.1076_0.784 | TRUE | 10 |
| 572.1094_3.326 | TRUE | 28 |
| 572.2105_4.512 | TRUE | 20 |
| 572.2528_4.339 | TRUE | 4 |
| 573.1087_0.695 | TRUE | 1 |
| 573.2393_7.26 | TRUE | 29 |
| 573.2395_6.305 | TRUE | 22 |
| 574.1142_2.883 | TRUE | 1 |
| 574.1523_2.913 | TRUE | 19 |
| 574.1534_3.396 | TRUE | 14 |
| 576.1317_0.792 | TRUE | 18 |
| 576.1384_0.705 | TRUE | 5 |
| 577.1184_2.969 | TRUE | 14 |
| 579.0437_2.578 | TRUE | 14 |
| 579.0457_0.796 | TRUE | 10 |
| 579.0995_6.236 | TRUE | 35 |
| 579.126_2.604 | TRUE | 9 |
| 579.2066_4.211 | TRUE | 14 |
| 580.1557_4.511 | TRUE | 35 |
| 583.0211_3.647 | TRUE | 5 |
| 583.1089_3.742 | TRUE | 1 |
| 583.1092_3.147 | TRUE | 9 |
| 584.0883_0.697 | TRUE | 5 |
| 584.1374_3.972 | TRUE | 19 |
| 585.0051_3.809 | TRUE | 4 |
| 585.0184_3.643 | TRUE | 2 |
| 585.0899_0.796 | TRUE | 12 |
| 589.3024_5.037 | TRUE | 21 |
| 590.1068_3.469 | TRUE | 5 |
| 590.1471_2.657 | TRUE | 37 |
| 591.2109_6.51 | TRUE | 6 |
| 591.3184_5.021 | TRUE | 16 |
| 593.1471_3.747 | TRUE | 21 |
| 593.1477_3.228 | TRUE | 35 |
| 593.3333_5.048 | TRUE | 15 |
| 594.0962_3.363 | TRUE | 1 |
| 594.3642_5.768 | TRUE | 4 |
| 596.1248_2.733 | TRUE | 1 |
| 599.1056_3.199 | TRUE | 26 |
| 600.2843_4.732 | TRUE | 4 |
| 600.982_3.815 | TRUE | 4 |
| 601.2255_5.243 | TRUE | 4 |
| 602.1667_4.638 | TRUE | 37 |
| 603.0611_5.431 | TRUE | 21 |
| 605.1126_3.023 | TRUE | 12 |
| 605.1462_3.238 | TRUE | 31 |
| 605.1484_3.739 | TRUE | 23 |
| 606.1191_4.053 | TRUE | 11 |
| 606.9991_3.815 | TRUE | 3 |
| 607.0749_3.657 | TRUE | 15 |
| 607.1267_3.483 | TRUE | 4 |
| 607.1628_3.711 | TRUE | 26 |
| 607.1633_3.738 | TRUE | 25 |
| 608.1059_3.3 | TRUE | 13 |
| 609.0591_2.577 | TRUE | 17 |
| 609.0726_3.66 | TRUE | 20 |
| 609.1423_0.802 | TRUE | 21 |
| 609.1484_2.937 | TRUE | 3 |
| 610.164_2.859 | TRUE | 2 |
| 611.0554_2.581 | TRUE | 25 |
| 611.069_3.647 | TRUE | 15 |
| 613.1932_6.507 | TRUE | 16 |
| 614.1849_4.02 | TRUE | 31 |
| 617.1836_4.515 | TRUE | 12 |
| 618.1838_3.802 | TRUE | 38 |
| 618.1913_4.649 | TRUE | 37 |
| 621.1797_5.389 | TRUE | 30 |
| 622.0984_3.522 | TRUE | 33 |
| 623.0319_2.6 | TRUE | 14 |
| 623.1618_2.946 | TRUE | 22 |
| 623.2016_6.501 | TRUE | 17 |
| 625.0354_3.814 | TRUE | 10 |
| 629.2176_6.511 | TRUE | 9 |
| 631.0532_3.815 | TRUE | 16 |
| 631.1154_0.686 | TRUE | 4 |
| 633.1142_3.808 | TRUE | 19 |
| 638.0934_3.724 | TRUE | 39 |
| 639.0783_3.758 | TRUE | 17 |
| 640.1347_3.515 | TRUE | 30 |
| 645.1099_2.832 | TRUE | 16 |
| 645.1108_0.776 | TRUE | 18 |
| 645.1887_2.968 | TRUE | 19 |
| 645.1893_2.881 | TRUE | 11 |
| 645.1896_3.451 | TRUE | 2 |
| 645.1901_2.522 | TRUE | 40 |
| 649.0893_3.836 | TRUE | 27 |
| 650.1898_2.57 | TRUE | 45 |
| 651.3856_5.28 | TRUE | 4 |
| 653.1834_2.294 | TRUE | 44 |
| 655.1071_3.826 | TRUE | 19 |
| 656.1287_4.025 | TRUE | 40 |
| 657.1659_3.876 | TRUE | 17 |
| 657.1681_4.216 | TRUE | 26 |
| 659.1239_2.825 | TRUE | 27 |
| 659.1667_2.763 | TRUE | 2 |
| 659.2045_2.918 | TRUE | 23 |
| 659.2057_3.452 | TRUE | 2 |
| 661.1248_0.679 | TRUE | 16 |
| 662.1454_4.018 | TRUE | 41 |
| 662.3153_5.383 | TRUE | 9 |
| 666.2108_3.475 | TRUE | 11 |
| 673.1446_3.853 | TRUE | 29 |
| 673.1455_4.198 | TRUE | 36 |
| 673.2558_7.365 | TRUE | 32 |
| 675.1192_0.802 | TRUE | 28 |
| 675.1222_2.643 | TRUE | 22 |
| 675.1511_4.19 | TRUE | 21 |
| 675.1659_2.308 | TRUE | 40 |
| 675.2009_2.65 | TRUE | 41 |
| 675.9686_3.302 | TRUE | 4 |
| 678.1113_4.032 | TRUE | 39 |
| 679.1604_4.197 | TRUE | 19 |
| 679.1625_3.856 | TRUE | 24 |
| 680.165_3.608 | TRUE | 37 |
| 680.2306_4.136 | TRUE | 42 |
| 681.2357_4.115 | TRUE | 32 |
| 681.985_3.302 | TRUE | 1 |
| 685.341_5.775 | TRUE | 7 |
| 689.1166_4.213 | TRUE | 22 |
| 690.3154_4.751 | TRUE | 13 |
| 691.1349_0.67 | TRUE | 17 |
| 691.188_6.507 | TRUE | 17 |
| 691.3175_4.507 | TRUE | 21 |
| 691.3179_4.171 | TRUE | 10 |
| 692.1776_3.461 | TRUE | 32 |
| 692.32_4.516 | TRUE | 24 |
| 710.0906_4.208 | TRUE | 43 |
| 713.2993_4.51 | TRUE | 16 |
| 714.3024_4.193 | TRUE | 28 |
| 714.3028_4.511 | TRUE | 26 |
| 73.0294_5.277 | TRUE | 15 |
| 741.1856_3.019 | TRUE | 40 |
| 741.1857_3.018 | TRUE | 40 |
| 741.1865_0.791 | TRUE | 36 |
| 745.1266_4.269 | TRUE | 35 |
| 745.1653_3.9 | TRUE | 38 |
| 747.2342_3.15 | TRUE | 44 |
| 747.2354_3.721 | TRUE | 40 |
| 747.3107_5.769 | TRUE | 21 |
| 751.1822_3.898 | TRUE | 37 |
| 753.328_5.771 | TRUE | 19 |
| 755.1981_3.389 | TRUE | 23 |
| 755.2012_3.03 | TRUE | 41 |
| 757.1576_0.789 | TRUE | 35 |
| 765.197_4.127 | TRUE | 29 |
| 769.2167_3.733 | TRUE | 41 |
| 769.2171_3.155 | TRUE | 43 |
| 771.1944_2.999 | TRUE | 41 |
| 771.1962_0.744 | TRUE | 18 |
| 775.1556_3.475 | TRUE | 1 |
| 779.2721_4.515 | TRUE | 31 |
| 785.1841_3.18 | TRUE | 44 |
| 785.1891_3.723 | TRUE | 40 |
| 785.2097_2.974 | TRUE | 39 |
| 792.3445_4.502 | TRUE | 41 |
| 795.2377_4.518 | TRUE | 30 |
| 800.1466_3.728 | TRUE | 38 |
| 800.2743_3.027 | TRUE | 43 |
| 801.154_3.74 | TRUE | 35 |
| 801.2049_2.656 | TRUE | 38 |
| 801.2549_4.519 | TRUE | 26 |
| 807.2393_4.206 | TRUE | 34 |
| 810.9784_3.814 | TRUE | 10 |
| 813.255_4.198 | TRUE | 31 |
| 814.2904_3.028 | TRUE | 42 |
| 814.3239_4.509 | TRUE | 42 |
| 815.077_4.202 | TRUE | 37 |
| 816.9958_3.812 | TRUE | 8 |
| 817.2886_4.438 | TRUE | 32 |
| 819.2194_3.7 | TRUE | 42 |
| 828.3048_3.061 | TRUE | 44 |
| 829.2715_2.727 | TRUE | 7 |
| 829.2715_2.758 | TRUE | 3 |
| 833.2468_4.513 | TRUE | 20 |
| 835.0489_2.8 | TRUE | 15 |
| 835.1973_3.718 | TRUE | 41 |
| 835.2887_2.754 | TRUE | 2 |
| 835.2888_2.725 | TRUE | 2 |
| 841.2146_3.693 | TRUE | 42 |
| 845.2477_4.186 | TRUE | 35 |
| 853.2516_4.121 | TRUE | 36 |
| 855.1197_2.99 | TRUE | 30 |
| 855.1238_0.789 | TRUE | 36 |
| 855.1239_0.785 | TRUE | 36 |
| 859.0857_3.824 | TRUE | 35 |
| 860.2066_3.476 | TRUE | 1 |
| 861.1356_3.001 | TRUE | 34 |
| 861.1404_0.799 | TRUE | 38 |
| 869.3936_6.15 | TRUE | 42 |
| 875.1494_2.993 | TRUE | 31 |
| 883.1412_3.819 | TRUE | 37 |
| 883.1453_3.774 | TRUE | 40 |
| 883.1498_3.145 | TRUE | 43 |
| 889.161_3.783 | TRUE | 39 |
| 889.1651_3.147 | TRUE | 41 |
| 891.2154_3.643 | TRUE | 42 |
| 891.2172_4.205 | TRUE | 36 |
| 892.2207_4.211 | TRUE | 36 |
| 896.2543_2.86 | TRUE | 18 |
| 907.1941_3.871 | TRUE | 42 |
| 907.1956_4.201 | TRUE | 37 |
| 908.2639_4.165 | TRUE | 43 |
| 913.2113_4.211 | TRUE | 34 |
| 913.2118_3.895 | TRUE | 40 |
| 913.214_3.531 | TRUE | 25 |
| 931.1224_2.809 | TRUE | 11 |
| 945.1645_3.008 | TRUE | 40 |
| 951.181_3.008 | TRUE | 39 |
| 973.198_3.133 | TRUE | 43 |
| 979.2122_3.736 | TRUE | 43 |
| 979.2124_3.136 | TRUE | 44 |
| 997.2507_3.723 | TRUE | 43 |
Remove NAs
Data imputation
# impute any missing values by replacing them with 1/2 of the lowest peak height value of a feature (i.e. in a row).
imputed_omicsdata <- omicsdata
imputed_omicsdata[] <- lapply(imputed_omicsdata,
function(x) ifelse(is.na(x),
min(x, na.rm = TRUE)/2, x))
dim(imputed_omicsdata)## [1] 1409 58Are there any NAs?
## [1] 0Create new imputed tidy datasets
# create long df for imputed omics df
imputed_omicsdata_tidy <- imputed_omicsdata %>%
pivot_longer(cols = 3:ncol(.),
names_to = "sample_ID",
values_to = "peak_height")
# combine meta and imputed omics dfs
imp_meta_omics <- full_join(imputed_omicsdata_tidy,
metadata,
by = c("sample_ID" = "sample_ID"))
# separate mz and rt
imp_meta_omics_sep <- imp_meta_omics %>%
separate(col = mz_rt,
into = c("mz", "rt"),
sep = "_")
# convert columns to correct type
imp_meta_omics_sep$mz <- as.numeric(imp_meta_omics_sep$mz)
imp_meta_omics_sep$rt <- as.numeric(imp_meta_omics_sep$rt)
imp_meta_omics_sep$Subject <- as.character(imp_meta_omics_sep$Subject)
imp_meta_omics_sep$Intervention <- as.character(imp_meta_omics_sep$Intervention)Notame feature reduction
vignette for reference
Let’s look at what masses come up at each RT again
# rt vs mz plot
imp_meta_omics_sep %>%
ggplot(aes(x = rt, y = mz)) +
geom_point() +
theme_minimal() +
labs(x = "RT (min)",
y = "mz")
There are several points that are at the same RT, meaning they could be
coming from the same compound. We’ll run notame clustering to collapse
features coming from one mass into one feature.
Data restructuring for notame
# create features list from imputed data set to only include unique feature ID's (mz_rt), mz and RT
features <- imp_meta_omics_sep %>%
cbind(imp_meta_omics$mz_rt) %>%
rename("mz_rt" = "imp_meta_omics$mz_rt") %>%
dplyr::select(c(mz_rt, mz, rt)) %>%
distinct() # remove the duplicate rows
# create a second data frame which is just imp_meta_omics restructured to another wide format
data_notame <- data.frame(imputed_omicsdata %>%
dplyr::select(-row_ID) %>%
t())
data_notame <- data_notame %>%
tibble::rownames_to_column() %>% # change samples from rownames to its own column
row_to_names(row_number = 1) # change the feature IDs (mz_rt) from first row obs into column namesCheck structures
## 'data.frame': 1409 obs. of 3 variables:
## $ mz_rt: chr "236.065_0.603" "168.0777_0.606" "154.0621_0.609" "124.0072_0.616" ...
## $ mz : num 236 168 154 124 193 ...
## $ rt : num 0.603 0.606 0.609 0.616 0.617 0.622 0.654 0.627 0.629 0.629 ...## mz_rt 236.065_0.603 168.0777_0.606 154.0621_0.609 124.0072_0.616
## 2 6111_U4_C18NEG_42 4148.1560 6707.0720 304457.6000 63058.5270
## 3 6101_U2_C18NEG_30 65118.8000 81841.7800 112404.4300 22063.8480
## 4 6102_U1_C18NEG_26 16741.8500 29358.3710 168966.0200 56236.6450
## 5 6110_U2_C18NEG_34 16350.678 19073.940 67706.220 22557.037
## 6 6105_U4_C18NEG_47 46170.5080 59856.3360 208992.6000 56521.9000
## 7 6103_U4_C18NEG_53 62220.0470 90745.4140 92892.3000 4167.1500
## 193.0351_0.617 239.1144_0.622 275.056_0.654 195.051_0.627 215.0326_0.629
## 2 110661.2340 4148.8400 7621.4624 175922.4200 83845.8400
## 3 50736.0040 13944.5030 10538.4540 222889.0500 112802.2000
## 4 54822.6170 13815.2390 14388.4710 185187.0500 93607.8900
## 5 27333.799 2379.748 12803.501 97982.840 72868.770
## 6 51904.7270 13813.5900 54594.4060 119151.6250 87575.5400
## 7 81048.3900 7217.6150 11000.1140 109782.7340 56883.4100
## 219.0457_0.629 217.0486_0.63 245.0433_0.63 165.0405_0.634 187.0383_0.634
## 2 14710.4650 42995.4000 22739.1500 84372.0550 17612.5400
## 3 21731.1640 67756.3750 13593.9990 129945.7300 16133.5760
## 4 16508.4900 46851.0430 11583.4240 62910.2600 16036.7840
## 5 29380.190 84598.010 9735.333 99338.500 14608.523
## 6 18316.0660 49151.7800 15733.0520 81935.9700 21083.0700
## 7 25524.6640 80661.0600 13465.1520 79159.3200 22429.1930
## 265.0732_0.64 235.057_0.641 135.0295_0.64 277.0852_0.658 179.0556_0.643
## 2 3554.0090 10549.7070 215612.5300 1896.8904 49858.5800
## 3 2770.6050 7714.5710 161603.8300 1777.6233 69490.1100
## 4 3590.4802 13949.0800 180550.1400 1650.3148 54854.7340
## 5 2833.232 2971.846 118229.020 1515.141 79040.130
## 6 2556.3813 5410.3740 180194.6700 1872.5413 66115.3360
## 7 1964.5671 4466.6120 110849.0160 1644.7844 34921.9960
## 149.0089_0.649 547.1106_0.67 149.0452_0.654 262.0364_0.654 321.0625_0.656
## 2 59227.8160 1480.1112 53766.4000 24966.1520 10882.5720
## 3 94413.3700 1979.9187 55440.1950 16337.3920 25443.6100
## 4 8282.0510 2248.3880 50711.7000 22093.7560 18592.2230
## 5 14616.016 1228.672 64062.992 35391.867 8272.857
## 6 3099.2580 1445.8310 53487.2930 18773.7660 11248.7210
## 7 8325.3300 1888.4089 32421.2910 79602.7660 6367.6733
## 174.0405_0.655 291.0683_0.654 167.0208_0.657 261.039_0.66 494.1144_0.666
## 2 21986.1740 3920.8584 391255.3400 21221.8500 1378.3564
## 3 22722.2850 3144.5125 352507.1600 33641.8670 1788.4076
## 4 22790.8480 2798.2664 404390.6600 20416.8710 1299.2500
## 5 16850.508 4256.903 614587.000 31758.988 2543.849
## 6 39613.5160 3506.3880 847425.0000 20707.1170 111281.9900
## 7 16621.3980 3902.8433 378931.6000 55393.7800 1669.5349
## 308.0983_0.658 247.062_0.659 151.0066_0.66 363.0756_0.658 277.052_0.659
## 2 17307.6640 25386.6520 8439.7650 11992.5160 8896.5530
## 3 15982.8930 19120.8850 11509.5250 37390.3870 5537.2305
## 4 21815.3630 39358.4380 12856.6350 36373.7030 8356.7295
## 5 11636.194 16349.409 18192.979 21776.125 12400.021
## 6 15389.8410 27319.9410 10566.3160 19312.0000 7137.5390
## 7 10550.2650 6860.6650 17224.1970 12659.9000 7859.8510
## 262.0286_0.664 287.0195_0.66 290.0877_0.662 287.0526_0.66 263.0573_0.662
## 2 21046.4430 14930.0260 8933.0620 7753.7603 15739.0200
## 3 33996.1300 7956.3467 4929.5070 7766.6333 17729.2660
## 4 27414.6350 9422.4240 8266.6430 8011.1235 17002.3710
## 5 33476.040 14959.646 6859.244 5627.218 11740.802
## 6 22452.0600 10694.9580 9186.9240 6865.4946 17202.9410
## 7 27361.5570 14248.4060 4465.6550 5874.4560 6983.8990
## 253.9903_0.661 188.0021_0.665 337.0436_0.684 300.039_0.661 461.0646_0.667
## 2 11407.4400 18472.0530 500.0201 22658.5160 1432.1871
## 3 7760.9200 56319.2970 4015.7778 24527.4630 3294.4424
## 4 11899.6250 12348.4490 4727.2830 27361.3520 2893.5520
## 5 8558.486 17131.700 3151.240 12541.503 1607.297
## 6 9068.2350 20309.2770 2994.2952 32732.2950 25902.2270
## 7 11395.0340 10298.0230 3248.6460 13565.7080 1928.0118
## 463.0657_0.669 295.0277_0.684 379.0686_0.661 491.0719_0.671 247.9959_0.702
## 2 1728.1648 3877.8640 19467.2250 1976.4014 9789.6280
## 3 3113.4307 3143.3470 126441.6600 1952.8645 26527.5180
## 4 2402.4695 4319.6396 77050.3800 1530.9227 9697.3850
## 5 1732.253 2029.218 27472.498 1314.838 11553.704
## 6 3606.9004 3085.3591 23418.0000 2655.3190 7443.9090
## 7 2762.2295 2343.9160 23161.1040 1687.4609 6933.9126
## 243.0617_0.662 351.0566_0.663 373.0567_0.663 557.1344_0.671 351.0199_0.667
## 2 30853.5410 32206.3120 7283.2095 1498.1433 3787.9624
## 3 23710.4650 19565.9750 11812.0510 1949.1074 15992.7295
## 4 34791.4770 32150.5210 10058.4360 1646.7344 18490.9430
## 5 29982.627 14812.694 3590.756 1011.158 6557.495
## 6 31734.1230 32706.4100 5388.3020 5968.7896 4365.3830
## 7 22360.3120 14061.2300 4312.7075 1533.3982 9119.1740
## 323.0417_0.656 396.025_0.663 293.0301_0.685 254.9816_0.664 389.0688_0.663
## 2 4656.8800 36380.0300 4546.7780 72849.4100 10816.2230
## 3 5719.6772 32557.7970 7261.9634 78899.5800 26806.1270
## 4 2628.1118 22872.4800 6610.0260 112633.5400 24806.8180
## 5 3849.919 9591.220 2736.389 38314.094 8937.354
## 6 6543.9297 21491.2710 34597.0470 60320.0200 12854.0020
## 7 2698.0030 14241.1330 11340.4300 33674.3950 5623.1020
## 328.0474_0.665 275.0569_0.663 445.0903_0.667 289.0702_0.663 293.0676_0.65
## 2 7255.3096 11471.7020 8148.3910 9563.9830 10029.2730
## 3 6964.9727 10538.4540 18728.7900 6446.3804 14552.4390
## 4 7752.7600 14388.4710 18968.0300 9187.5260 10567.3660
## 5 4605.590 12803.501 4771.278 8563.401 10739.345
## 6 6006.5950 54594.4060 10714.8250 7585.1740 12973.7400
## 7 4763.8040 11000.1140 7042.9060 4399.0260 8009.9320
## 541.1207_0.663 227.9967_0.668 395.0628_0.675 405.0648_0.668 398.0412_0.681
## 2 3575.8132 72074.0860 12726.3030 6694.6963 2889.9944
## 3 10819.1610 72658.7400 53118.3320 34385.7070 2812.6152
## 4 8255.3910 45834.5980 31870.0040 23018.1110 3777.0356
## 5 2170.591 19956.219 15352.704 8227.380 8131.128
## 6 5221.6914 34732.7850 15376.1310 10411.9450 268753.4700
## 7 15615.9230 26029.3890 23932.9200 7877.4985 4579.8630
## 231.0308_0.666 289.0528_0.664 375.054_0.665 225.0623_0.658 360.0729_0.672
## 2 12636.0080 9199.4980 8978.2620 3959.2925 9899.3890
## 3 24950.5680 6697.9746 29421.7200 7518.0060 31006.0350
## 4 23874.4650 7208.1510 20557.3120 23502.8870 24456.6840
## 5 11439.280 7413.016 9835.665 2361.148 8538.419
## 6 18868.8100 7997.8090 9912.5580 17117.3850 16410.5250
## 7 13173.4400 4967.0054 6042.7983 8349.9020 6997.9180
## 216.0196_0.667 261.0232_0.668 260.0254_0.667 347.059_0.674 307.0116_0.682
## 2 58641.7500 173269.6200 117672.5900 10468.3830 1521.8510
## 3 178319.8600 340208.4000 203477.6100 30192.1100 2271.5900
## 4 157426.6400 316508.0000 211304.9400 22496.0840 2083.1924
## 5 69920.414 273429.530 165951.300 23684.217 2951.281
## 6 80708.1640 229276.4700 170327.8100 30482.3240 5467.7190
## 7 124550.2400 317140.1000 192901.4400 22400.6250 6189.2007
## 356.031_0.667 129.0191_0.669 331.0274_0.668 85.0291_0.668 345.0438_0.668
## 2 6124.0454 236005.0800 21954.3520 84031.9300 37873.7230
## 3 18968.3570 329562.6600 19095.8890 110775.6500 72203.1600
## 4 4514.1060 312758.8400 18446.1100 116002.2600 108276.0200
## 5 6339.006 229952.780 24770.727 83288.960 26113.564
## 6 4446.5310 295936.3400 12468.6190 108941.4450 40104.8100
## 7 7339.7754 297184.9400 40800.0550 91490.1700 15130.1990
## 336.0493_0.662 217.0162_0.671 349.059_0.669 371.0241_0.671 359.0566_0.67
## 2 21169.9020 254774.3800 26902.2930 3997.8467 11019.8160
## 3 20621.5720 666478.4400 74113.7340 10361.5530 15147.1770
## 4 20894.6400 575733.5000 41118.1640 9828.1280 17454.2360
## 5 41425.480 245340.300 27493.342 2132.876 8432.428
## 6 50127.7600 298069.7000 27595.8140 2971.7940 11856.2480
## 7 21760.6640 460064.8800 19456.7030 5593.3013 9593.0750
## 475.0782_0.667 432.2024_0.669 246.0075_0.694 304.0334_0.669 305.0302_0.67
## 2 2270.4578 500.0201 3329.6702 104681.8050 147793.8300
## 3 4959.0230 502.2117 3286.5608 137142.9700 184741.3800
## 4 4681.2690 500.2725 2274.4524 153807.8800 189132.4200
## 5 2710.678 500.000 5420.414 136424.440 180358.340
## 6 2697.7890 2117.2908 131341.4200 107177.8700 153268.7800
## 7 3094.5256 502.2908 2874.5503 163900.8400 203005.8400
## 431.0534_0.666 274.0026_0.671 230.0493_0.666 550.1307_0.664 173.0089_0.672
## 2 5363.1494 7800.1600 2385.2058 1647.1179 387825.7500
## 3 11396.4760 56630.1370 2519.8853 2237.8118 498700.2200
## 4 8234.1190 6476.7000 2632.7715 2314.8562 448936.1200
## 5 4677.412 16356.477 2170.819 1977.353 354741.720
## 6 10177.3270 8834.4010 24179.0920 1834.4805 437558.3400
## 7 5358.1304 28521.2150 2795.7686 1410.5865 458486.2500
## 526.0995_0.675 232.005_0.705 330.0291_0.673 410.0417_0.684 301.0547_0.665
## 2 1524.6020 4428.5107 21943.5060 23547.4300 4664.0024
## 3 1872.9838 6473.8890 19465.5020 12721.8960 3882.1885
## 4 1563.8273 4951.8720 17778.1050 2655.2402 4994.1500
## 5 2444.157 3688.011 33552.070 2591.827 3447.831
## 6 141841.3300 48300.5200 17316.7400 5945.2915 29855.4650
## 7 1605.1570 2360.2800 46560.2300 5217.2180 17143.3900
## 274.0396_0.676 691.1349_0.67 247.9921_0.704 318.064_0.674 315.0338_0.673
## 2 12929.9360 1078.6835 9789.6280 7785.8174 8878.0050
## 3 12111.8170 502.2117 26527.5180 10388.0580 18749.4880
## 4 11576.7710 1015.5614 9697.3850 10731.3760 22687.9200
## 5 12632.411 1233.036 11697.337 8062.857 13496.725
## 6 13121.7830 2851.9258 4977.2817 7053.5703 11470.7850
## 7 10873.2240 1045.9828 7218.6430 5680.6353 12024.8520
## 320.0597_0.683 277.0349_0.667 338.1134_0.651 462.0712_0.669 262.9863_0.693
## 2 28297.3900 4416.8910 1313.6183 1579.1161 2423.9880
## 3 30200.4860 9214.7320 1212.6208 3230.3440 3717.8792
## 4 21522.0180 4337.5625 1055.6311 2531.1738 2146.2593
## 5 12583.481 4946.517 1185.092 1835.917 3136.109
## 6 15244.0340 5060.5557 1195.8912 6311.4970 4296.7314
## 7 13343.0760 3963.0884 1609.4896 1764.8909 1933.5741
## 264.9884_0.68 399.0203_0.683 344.0448_0.677 303.0365_0.684 365.0528_0.68
## 2 113964.4200 6803.4350 24700.0740 28172.1950 9602.5650
## 3 112022.4400 33700.0700 50612.1680 37815.9730 33895.2850
## 4 131204.6700 27662.9690 58156.9180 56812.6330 11426.0930
## 5 57290.050 19677.950 17857.010 38021.715 20620.957
## 6 154751.2000 12679.5990 29855.4840 48059.8830 13932.0040
## 7 95716.9800 37637.4400 17081.1860 42261.2340 10952.6470
## 307.0326_0.669 246.0433_0.669 275.0216_0.69 288.0231_0.685 278.0698_0.683
## 2 7651.1260 6301.9400 21675.7010 2171.0352 11119.6875
## 3 12801.0280 4495.1990 48333.0800 2594.4520 12375.6870
## 4 9367.3400 5753.6040 46051.7800 3280.4750 9924.6080
## 5 10809.471 6208.385 27149.668 7146.336 12335.614
## 6 6298.2440 9574.2030 52112.7900 2866.7534 8318.3960
## 7 13549.3100 6263.1255 27632.2200 6015.0310 10469.7580
## 218.9961_0.686 288.0398_0.684 417.0297_0.684 191.0212_0.699 509.0384_0.699
## 2 29057.5620 5254.1343 36776.4770 4271292.5000 9943.6940
## 3 26525.7500 14368.8700 74917.3500 5971350.0000 14769.3430
## 4 17512.9430 12189.8940 71361.4600 5661172.0000 500.2725
## 5 69096.266 6233.876 46809.650 5800687.000 1152.252
## 6 49340.2930 11836.9680 45453.7730 6219833.0000 500.0225
## 7 45610.5160 8530.9200 80406.5100 5948255.5000 1457.8425
## 265.0186_0.67 289.0363_0.688 300.0195_0.687 661.1248_0.679 436.092_0.669
## 2 5677.8240 21162.4040 6127.3200 500.0201 1625.5333
## 3 5042.4697 38123.3830 16401.1930 1504.8782 2302.1897
## 4 5974.3800 38736.9300 17186.1000 500.2725 2267.7693
## 5 9163.784 25467.670 14968.058 500.000 500.000
## 6 10135.6860 32850.7230 10955.5780 1238.6486 1547.0419
## 7 9497.1460 29641.8030 29918.4690 1207.5732 6623.4727
## 480.0818_0.682 419.1179_0.71 361.0578_0.686 242.0115_0.695 385.0951_0.677
## 2 1683.9724 4461.0713 22139.1600 76768.0000 1877.1893
## 3 2538.0703 5500.9530 37329.9960 36109.5940 1451.5579
## 4 2445.1873 3409.8867 37392.1130 3506.0010 1697.6648
## 5 1214.369 2178.181 13581.197 4123.630 1439.647
## 6 3476.8518 5135.9530 37359.7400 6403.4080 1721.6453
## 7 1833.8857 4428.6490 22033.4470 4559.3870 1516.4857
## 487.0783_0.683 145.0141_0.693 631.1154_0.686 302.0532_0.694 439.056_0.753
## 2 2089.9312 32694.4600 1107.0416 19273.2070 3057.6484
## 3 3229.2050 27103.3890 502.2117 17813.2620 2580.3770
## 4 1752.8016 39290.8360 1138.2074 25512.5590 1636.4819
## 5 1377.679 18083.127 500.000 11645.039 1544.656
## 6 4552.8564 29402.8220 1328.6703 18164.5120 2300.6042
## 7 2126.6877 48293.8800 1027.8997 12906.6530 3150.1926
## 418.1123_0.717 377.0526_0.697 296.9824_0.699 218.0492_0.684 332.0663_0.696
## 2 9240.7580 16165.5120 172356.9800 6156.4100 14352.7540
## 3 27600.7580 22953.5960 261831.1000 86759.5400 11513.1870
## 4 8733.8000 17618.7130 260996.6400 9987.9600 14640.1580
## 5 10812.652 18719.190 141970.620 20545.275 13763.928
## 6 20853.9500 24687.9770 211734.0300 9220.2070 11579.3830
## 7 9241.5000 10513.1230 254820.1000 32551.0740 10625.5540
## 133.0504_0.696 405.0469_0.697 349.0207_0.696 295.9856_0.697 527.1244_0.676
## 2 58184.8870 12246.4560 9556.4840 44239.1370 1520.0815
## 3 19859.4530 20985.3090 21081.8090 68629.1500 1453.9219
## 4 26414.5860 20242.2850 2572.4692 71051.2900 1478.5604
## 5 39528.965 22405.697 2777.294 36044.312 3086.462
## 6 27557.3850 21118.8800 6357.4443 65073.4400 6357.5200
## 7 40625.3830 17682.3480 1964.0250 64934.2340 1952.4695
## 333.0634_0.694 392.0363_0.698 570.0904_0.696 394.06_0.685 144.03_0.697
## 2 50376.0160 79644.3200 1192.5267 5016.3830 20549.5370
## 3 37198.0000 211934.0500 1945.4053 17367.4360 12359.0590
## 4 45505.5430 195954.0600 1329.6543 10407.0420 17822.9410
## 5 36155.800 111624.734 1486.894 11816.269 14422.314
## 6 44392.8600 216734.2000 1282.2678 6937.3223 18463.6300
## 7 27413.2900 155174.6400 4704.6973 47895.9340 12584.4480
## 391.0332_0.698 296.0197_0.697 191.0523_0.699 319.0474_0.696 347.0424_0.697
## 2 493382.7800 2065.5852 128032.3000 228269.9700 21344.0270
## 3 1598408.4000 1706.8990 127600.2340 207429.1600 76096.5860
## 4 1336129.4000 2461.0554 114005.5700 330992.1000 35181.4700
## 5 737906.100 1892.521 113870.650 140869.950 32082.860
## 6 1615176.4000 1539.0139 104292.5550 204233.7800 27753.1290
## 7 654401.1000 2343.5354 148865.5500 66676.6400 25022.4880
## 316.0285_0.688 191.0203_0.697 192.0224_0.702 320.05_0.698 397.0582_0.701
## 2 2335.3386 4271292.5000 244912.1200 32589.7360 17310.6840
## 3 17480.0800 5971350.0000 425455.5300 30232.3830 23589.9060
## 4 2175.9163 5661172.0000 382120.6600 42970.6250 27858.7800
## 5 3997.198 5800687.000 510942.700 19847.176 12179.876
## 6 2973.0570 6219833.0000 455817.3400 31299.6930 20153.5600
## 7 5113.0913 5006305.0000 402905.8800 11344.9470 14181.0670
## 277.0021_0.707 206.9961_0.7 78.9585_0.702 468.0451_0.696 389.0528_0.7
## 2 5372.9487 52142.7700 211444.3600 1784.0773 26811.8100
## 3 10250.4440 135124.9400 170654.5800 3806.7124 41203.8750
## 4 3510.5737 62368.0500 219096.4800 1811.3746 37091.0940
## 5 2991.485 68935.340 103807.100 5802.637 14793.726
## 6 12172.9250 78885.2600 189029.5500 3278.9207 24250.2680
## 7 4614.7040 83022.6900 134074.3300 11802.0830 21201.1930
## 246.9913_0.705 161.988_0.685 154.9984_0.703 246.9916_0.705 111.0085_0.703
## 2 81910.3050 500.0201 29219.2500 81910.3050 406520.7800
## 3 229988.0200 367110.0000 25145.9770 229988.0200 661080.1000
## 4 73131.0160 1071.1361 22682.8790 73131.0160 603447.7500
## 5 85781.720 9216.258 15626.091 85781.720 626823.200
## 6 32668.5900 3869.4077 21313.9730 32668.5900 730377.2500
## 7 41336.6950 502.2908 15210.5540 41336.6950 634365.5000
## 216.981_0.709 524.0845_0.705 427.0674_0.685 379.0336_0.708 367.0282_0.707
## 2 60342.5200 6745.0120 2228.5696 15370.5170 23634.7270
## 3 105255.5160 6333.3228 2495.8103 43376.4340 15567.3000
## 4 42331.0080 5804.3696 2213.4170 19405.0370 15214.2550
## 5 48391.125 1876.439 2423.096 12354.700 18005.928
## 6 127584.3300 6225.2646 4249.3784 14402.4060 17370.8000
## 7 101494.0860 17141.7000 3508.2046 23399.6170 7441.6300
## 543.0792_0.704 303.0532_0.705 225.0424_0.708 232.9759_0.708 423.0571_0.703
## 2 9441.3060 33108.5700 1438.5791 33282.6800 6167.0180
## 3 3902.8647 45647.2970 1193.7743 69228.0100 44044.9650
## 4 6051.2640 66692.6950 3760.6030 18993.3800 12221.2400
## 5 3249.885 41528.293 1397.930 41496.670 8118.665
## 6 2081.1350 45342.8480 1462.1958 23995.3670 45888.8950
## 7 7330.2363 37758.7660 3271.2617 36132.2150 18807.6430
## 347.079_0.706 146.0456_0.708 335.0468_0.663 409.0428_0.71 559.1102_0.68
## 2 28352.6700 43757.4770 41948.9730 5476.0980 2089.9404
## 3 33714.1100 40495.8870 150743.6400 29388.1860 2526.5820
## 4 31311.3070 32793.0400 156294.3900 6958.3896 3685.1807
## 5 29898.604 29624.328 300009.060 7829.560 1578.039
## 6 27567.2190 58301.5940 55818.1170 30897.6580 18702.7520
## 7 20342.1930 34810.2200 29560.6330 12070.7570 2207.5564
## 259.057_0.712 377.0353_0.713 263.0249_0.69 370.0786_0.733 499.0756_0.705
## 2 20013.4550 19127.5500 16781.0500 3990.0160 1729.8732
## 3 7821.5845 47368.9000 22301.0590 3680.2607 1900.2081
## 4 16123.0690 43134.0660 19644.1150 3496.4058 1986.4675
## 5 8619.797 27326.040 25428.926 2746.170 3551.452
## 6 13171.8270 31398.5940 150805.6600 3453.3310 2036.7467
## 7 5871.9990 24756.8480 11801.4420 20682.4200 3225.7664
## 310.9761_0.719 291.0345_0.717 576.1384_0.705 424.0493_0.721 479.0535_0.737
## 2 55626.6900 22876.6720 500.0201 23092.5080 1715.2510
## 3 73050.5550 31448.6480 1191.1466 18950.0430 1446.0248
## 4 86704.8360 29264.3400 1416.7020 10549.7740 2122.0615
## 5 102870.610 36290.960 1044.005 8377.034 1198.108
## 6 77251.2340 30482.2050 2239.2495 19477.7250 15126.6670
## 7 63273.5600 18923.0550 1260.1454 9328.3270 1831.1595
## 405.0285_0.72 230.0139_0.763 418.1149_0.722 303.0715_0.727 258.0424_0.699
## 2 34730.8800 26786.1720 9386.8560 25215.3240 11518.4375
## 3 96463.8700 173433.3400 27600.7580 30536.9020 7196.1730
## 4 66619.4450 7272.1816 19513.6000 19851.1050 9132.3030
## 5 213321.170 31827.668 10812.652 31402.518 9889.745
## 6 91735.6600 1094556.0000 20853.9500 40252.7540 6244.5160
## 7 129526.1200 11000.6190 9241.5000 44833.5470 6408.3320
## 549.0364_0.757 485.0955_0.681 303.0712_0.728 584.0883_0.697 573.1087_0.695
## 2 1020.5068 1766.9160 25215.3240 1191.6068 500.0201
## 3 1264.0416 1969.4229 30536.9020 1020.0275 1564.3948
## 4 500.2725 2078.9668 19851.1050 1133.7792 1315.0110
## 5 1164.973 1888.713 31402.518 500.000 1212.008
## 6 1231.2000 2009.3416 40252.7540 1079.5731 5382.2840
## 7 502.2908 1626.0334 44833.5470 2581.0370 1605.2543
## 422.0501_0.727 218.1032_0.748 247.0277_0.755 175.0241_0.753 194.046_0.798
## 2 8559.4010 13785.0200 11656.1640 18934.5270 85857.4900
## 3 6211.2007 19853.5980 18421.3830 6923.8100 266435.7800
## 4 4910.7686 13012.5740 7439.3916 5896.4290 123845.4200
## 5 3516.970 11549.192 24980.500 9438.662 212657.140
## 6 11584.1590 19291.5940 12509.6540 23864.9260 100885.5550
## 7 13048.5540 14139.4060 40544.0820 16835.7190 788230.6000
## 137.0248_0.733 191.0524_0.713 205.0352_0.76 343.0666_0.756 457.0689_0.726
## 2 3329.1177 128032.3000 73709.0000 23513.5180 2072.7050
## 3 4851.5300 127600.2340 39592.6520 9829.7160 1468.5693
## 4 2741.8123 114005.5700 47546.7660 9161.8260 1876.0030
## 5 7323.842 113870.650 44800.914 4856.681 1510.607
## 6 5956.7850 104292.5550 87591.4000 3503.1530 14086.1220
## 7 13008.8050 126887.1900 49713.2460 19257.4450 2431.9690
## 147.0298_0.76 191.0267_0.755 389.1038_0.786 161.0454_0.768 855.1239_0.785
## 2 95357.3100 35133.3700 1946.2800 57661.7420 500.0201
## 3 135842.7200 34729.1130 20584.5350 53200.0800 502.2117
## 4 70525.4400 34061.3120 2451.4397 53321.2100 500.2725
## 5 93652.484 35948.690 5237.721 50538.830 500.000
## 6 72001.3750 32916.3400 17286.3790 49353.9900 1048.2848
## 7 86855.1900 41353.9380 44659.9920 62155.1170 502.2908
## 541.1395_0.742 446.038_0.779 177.0225_0.776 324.0723_0.776 208.0646_0.766
## 2 500.0201 1658.7102 40995.2600 68043.3300 8426.6540
## 3 1242.6123 2290.6530 46588.3870 57325.2270 8188.7827
## 4 1000.5450 1217.4502 47735.3200 34703.0800 4456.1580
## 5 500.000 2121.245 39499.200 22483.912 2965.903
## 6 2088.4001 38099.4000 43068.2660 42821.1200 6748.7620
## 7 1661.0409 2480.7651 39971.6600 163982.8800 5671.8830
## 757.1576_0.789 301.0563_0.774 1153.2827_0.789 144.0665_0.781 645.1108_0.776
## 2 500.0201 6962.6953 500.0201 51786.9380 1206.7730
## 3 502.2117 6924.0005 502.2117 34476.7150 1019.9524
## 4 500.2725 4752.0557 500.2725 42257.6300 500.2725
## 5 500.000 2565.265 500.000 28798.984 500.000
## 6 1481.4994 29855.4650 500.0225 35803.4180 1585.2367
## 7 1157.0000 14510.4170 502.2908 27630.0680 1248.0000
## 165.0409_0.76 555.1553_0.783 245.1137_0.783 299.1247_0.784 364.0954_0.768
## 2 19419.2190 500.0201 13970.7210 8509.2240 1520.5922
## 3 17942.3710 1373.9762 30608.9240 12404.6730 1945.4222
## 4 28789.5300 500.2725 14505.4890 9157.6420 1601.9349
## 5 17728.719 500.000 6751.876 11656.090 1604.762
## 6 19712.3380 2497.3308 20732.1620 11879.6970 1327.7667
## 7 31254.9900 1189.3865 20329.3360 14583.1960 2468.3755
## 357.0823_0.779 741.1865_0.791 195.0512_0.787 360.1406_0.756 315.0713_0.751
## 2 7354.2656 500.0201 29134.7600 2263.6921 3304.1526
## 3 4200.4453 502.2117 47263.9060 2437.9482 2395.3720
## 4 6396.3623 500.2725 73776.2340 2458.7043 2043.2003
## 5 7101.456 500.000 30895.540 1365.453 2464.498
## 6 4792.8300 2534.9316 70425.2900 2573.9521 2448.0850
## 7 34339.9300 502.2908 215220.3800 1706.7955 4512.0690
## 319.1032_0.789 274.039_0.769 275.0221_0.754 359.0971_0.789 204.9812_0.795
## 2 22483.9790 18499.6270 10979.9460 2741.6162 53297.8200
## 3 11512.6950 11861.7780 24789.8850 1889.9879 41683.9450
## 4 14819.9300 14183.1560 32565.0140 2321.9690 70088.2660
## 5 17165.516 11632.619 24247.920 1814.458 51446.676
## 6 17031.8540 11086.0630 20019.9770 168268.1700 104181.9600
## 7 25862.6720 13121.2560 16547.5940 4055.0860 110612.6800
## 216.9808_0.715 771.1962_0.744 231.9926_0.79 356.0987_0.796 261.0072_0.769
## 2 60342.5200 1552.0686 11582.2560 2576.5925 30946.5700
## 3 105255.5160 502.2117 47461.7930 1573.0795 68774.0800
## 4 42331.0080 500.2725 25121.1290 3326.7440 43543.4260
## 5 48391.125 500.000 17798.205 1769.738 47227.617
## 6 127584.3300 1172.4828 13655.8780 10235.5205 27382.3340
## 7 101494.0860 1336.9645 5845.7850 2619.3972 171906.8100
## 391.1124_0.79 194.0546_0.797 227.9971_0.795 502.2071_0.806 415.1607_0.797
## 2 2238.7780 8675.9680 66358.0000 500.0201 2236.2551
## 3 2061.1780 18655.8200 81859.7900 502.2117 6679.4690
## 4 7646.0225 9949.7650 50406.9200 500.2725 2686.2230
## 5 1121.088 15096.397 15420.121 500.000 2700.322
## 6 10854.7720 8582.5820 30611.0900 500.0225 2364.1380
## 7 36579.4600 38545.4500 44149.2030 502.2908 2757.7136
## 361.1497_0.795 303.0177_0.795 223.0623_0.802 189.9895_0.816 375.1294_0.798
## 2 4447.7236 7302.1000 1144.6578 10736.8950 7363.2485
## 3 21850.8500 14427.2920 1835.3335 19163.7540 28331.7320
## 4 7930.0034 11333.2390 1118.6530 13191.9130 14353.0000
## 5 3655.935 8702.116 1202.347 25967.732 4386.368
## 6 16740.7930 42028.5620 3526.4907 9589.3860 18520.5740
## 7 32214.5300 36042.9260 2448.0690 19991.5940 53226.5940
## 276.0018_0.763 168.0301_0.797 399.0923_0.803 373.0779_0.78 227.1036_0.798
## 2 1157.8638 36358.3050 2278.4138 2605.3170 12904.6310
## 3 3154.7210 50863.8120 2017.6725 1747.8801 25581.7870
## 4 2522.9702 48530.4400 2583.0005 3284.8716 15256.9740
## 5 9869.246 61832.360 2220.696 1572.836 7753.059
## 6 44971.0430 39271.9800 5329.5500 5729.1904 16874.2710
## 7 3434.5747 43985.0980 6257.0254 6013.5800 21659.8850
## 479.0506_0.791 411.0787_0.764 326.0866_0.798 579.0457_0.796 194.0459_0.799
## 2 500.0201 4575.4520 8678.5530 1057.3336 85857.4900
## 3 1241.6469 4025.5898 20011.0160 1393.7997 266435.7800
## 4 1161.4454 1995.3750 1661.0067 1024.1156 123845.4200
## 5 1198.108 3552.362 14005.729 500.000 212657.140
## 6 15126.6670 2133.0980 524976.2500 1327.6630 100885.5550
## 7 1831.1595 30593.5720 5637.0615 1434.3068 788230.6000
## 861.1404_0.799 1123.2723_0.775 516.2234_0.803 230.9967_0.757 282.0473_0.802
## 2 500.0201 500.0201 500.0201 26454.2830 5276.6020
## 3 502.2117 502.2117 502.2117 48949.5080 43431.7730
## 4 500.2725 500.2725 500.2725 37910.1800 10161.4830
## 5 500.000 500.000 500.000 30236.193 17003.963
## 6 1474.1422 500.0225 500.0225 28247.5180 34313.3100
## 7 502.2908 1337.8524 502.2908 34517.1330 25865.1460
## 182.0076_0.788 292.0225_0.799 258.9916_0.797 182.0282_0.77 219.0508_0.792
## 2 1016.8692 15912.0620 9436.8930 500.0201 2348.0452
## 3 1093.6797 38215.8320 27677.5310 502.2117 9981.2650
## 4 1148.9729 15948.4610 8003.4120 3617.5800 2965.5898
## 5 1069.306 19340.375 21220.290 1865.322 24056.643
## 6 17604.9840 11497.8000 15724.6510 57946.8830 2706.5703
## 7 502.2908 52938.4260 13266.9070 4012.2197 482739.4000
## 182.0458_0.803 489.0926_0.758 337.0556_0.777 389.1018_0.792 371.0976_0.801
## 2 38961.1700 1434.7361 77891.1100 2451.1028 6994.3535
## 3 51169.7730 1411.7039 2123.8315 20584.5350 3121.8787
## 4 33758.1400 1341.1239 3934.7075 2709.4160 7716.4140
## 5 25916.156 1399.844 5028.712 5237.721 5879.060
## 6 20724.7100 1986.8817 2222.3220 17286.3790 5209.3247
## 7 17853.4820 1288.3813 14473.3930 49781.4200 6424.0580
## 403.197_0.807 131.0349_0.8 372.0951_0.795 311.0753_0.804 230.0156_0.793
## 2 4133.2060 93725.9300 1870.9968 7916.0620 26786.1720
## 3 16434.2950 52997.4530 1706.7196 4291.2227 20267.7770
## 4 5214.3955 74554.6300 2679.1145 1645.0190 7272.1816
## 5 18957.777 29278.055 1695.265 4510.502 7118.002
## 6 4943.9473 62619.9600 3602.7686 101489.9840 194732.3800
## 7 3190.8542 34408.7730 2704.4630 5213.9630 13361.8400
## 181.9916_0.803 675.1192_0.802 248.0596_0.808 203.0021_0.801 230.0135_0.791
## 2 2235.6890 2395.5625 4100.9370 8536.0040 26786.1720
## 3 1330.1858 502.2117 94115.8100 13174.4260 173433.3400
## 4 1695.2194 500.2725 8698.7560 9756.9730 7272.1816
## 5 7599.906 500.000 12406.228 20716.230 20028.576
## 6 255537.9700 1356.0408 8838.0460 10468.7050 194732.3800
## 7 5933.5790 1036.9396 40463.0980 14179.2080 13361.8400
## 273.0073_0.807 313.0567_0.793 359.1353_0.806 227.06_0.796 576.1317_0.792
## 2 21640.5350 1576.8871 1199.9650 2327.6902 500.0201
## 3 55730.0160 2747.0770 2705.9120 1677.5560 502.2117
## 4 32845.9340 2117.7060 2024.0610 2125.5698 1130.5149
## 5 41429.938 1615.805 2194.423 2231.195 1044.005
## 6 56694.3000 2740.6160 5522.3070 18335.8570 2239.2495
## 7 43730.2500 8706.5710 2298.1697 1403.5505 1486.2402
## 368.0998_0.802 336.0708_0.807 224.0563_0.81 419.1134_0.748 855.1238_0.789
## 2 3468.8462 7069.7700 40847.9300 2662.7239 500.0201
## 3 2115.9365 5099.1740 11843.7440 5500.9530 502.2117
## 4 1713.5104 4117.1530 10996.3770 2803.2563 500.2725
## 5 1230.698 1282.872 11374.529 2390.166 500.000
## 6 1444.2819 4479.4530 8285.0800 4002.0244 1048.2848
## 7 1337.2166 3150.0024 16682.5500 4428.6490 502.2908
## 213.9982_0.833 188.9864_0.815 609.1423_0.802 572.1076_0.784 139.0401_0.786
## 2 2720.9587 162080.4400 500.0201 1015.9120 1527.6703
## 3 4570.8184 209628.9400 1545.8417 1006.0687 1350.2716
## 4 3981.9260 170851.8000 500.2725 500.2725 500.2725
## 5 2028.928 255497.620 500.000 500.000 1285.313
## 6 5432.6147 87560.8300 1138.2489 1088.1221 5499.4810
## 7 12856.6660 211470.3600 1402.1483 1305.2180 1352.0557
## 325.0323_0.804 181.0505_0.817 227.0559_0.8 124.01_0.775 422.1067_0.783
## 2 1310.0017 15064.9710 2327.6902 2415.2400 1149.3724
## 3 1439.7224 67733.4200 1840.9075 1614.5555 1933.4332
## 4 1111.0118 28290.8770 2125.5698 1725.7606 1274.4321
## 5 1023.366 18807.293 2467.409 1732.137 1027.562
## 6 2717.3884 16499.2660 18335.8570 2749.5370 1184.9493
## 7 28907.1250 94906.8500 1695.8068 502.2908 2038.3225
## 313.999_0.818 212.0023_0.825 448.1934_0.807 173.9923_0.799 585.0899_0.796
## 2 5934.9500 37405.9000 1357.0842 8043.6180 500.0201
## 3 14747.1230 90601.1100 502.2117 16555.2830 1217.2870
## 4 6826.1577 54552.4340 500.2725 16701.9960 500.2725
## 5 12779.035 24581.033 500.000 7784.886 500.000
## 6 5558.2880 99594.6800 2584.2312 5053.1750 1103.8805
## 7 24121.5040 173987.5600 2230.3474 17356.8090 1146.1119
## 463.107_0.79 477.1007_0.807 347.0076_0.814 167.0211_1.15 335.0494_1.152
## 2 3221.6296 1190.4961 1787.5654 403188.2500 119423.9800
## 3 2015.3980 502.2117 1232.8690 314273.6200 125768.9140
## 4 2161.7740 1062.6462 500.2725 334604.5300 134767.8800
## 5 1300.083 500.000 1404.900 373212.280 146170.770
## 6 2268.4602 1781.0600 1910.8651 600193.9400 270152.7200
## 7 2006.0029 1363.7043 1203.0910 460556.6000 205647.5000
## 381.0301_1.155 380.0301_1.155 217.0161_1.159 191.0201_1.159 405.0285_1.16
## 2 35170.8800 6337.1704 19659.6720 363765.7200 27292.4140
## 3 55100.8750 16793.7460 41126.7730 1733155.1000 150123.9700
## 4 51315.1500 10487.8180 42623.0350 1347743.6000 109597.8360
## 5 51177.840 5253.422 38632.848 1152099.200 100218.330
## 6 71307.1800 17658.1230 33021.2500 811499.5000 51973.1130
## 7 59260.8000 33215.3480 65129.5300 1213337.4000 98734.7900
## 111.0087_1.164 427.0103_1.161 310.9691_1.162 188.9863_1.166 191.0276_1.165
## 2 34893.6680 12452.2420 32047.7750 65642.7600 23857.4770
## 3 158133.1900 51556.8360 65223.3320 72952.6300 35074.5160
## 4 120755.1000 42546.7300 54402.0400 48730.1520 29036.6130
## 5 109486.630 34395.360 51852.625 109894.020 34110.137
## 6 82226.0860 23796.7990 41272.4060 31848.6350 37843.8700
## 7 121721.1200 37825.1050 51626.8600 76486.5160 34429.6250
## 173.0091_1.231 258.044_2.007 375.0931_2.077 227.0554_2.1 324.0723_2.134
## 2 30944.4840 2533.6267 500.0201 500.0201 21306.9510
## 3 44537.5080 502.2117 502.2117 502.2117 9048.3220
## 4 34525.3900 4374.3315 500.2725 500.2725 9504.1150
## 5 28947.867 4493.719 500.000 500.000 2797.101
## 6 29390.3050 1068.1229 500.0225 2764.5764 10496.8910
## 7 46178.5820 1863.5226 502.2908 502.2908 11165.8830
## 161.9866_2.133 182.0458_2.174 213.9981_2.254 326.0973_2.32 384.0537_2.335
## 2 500.0201 28041.9040 6752.2180 1109.6661 2353.7732
## 3 159573.4000 34700.8240 12230.5710 1026.8625 3245.9348
## 4 500.2725 19610.7710 5277.7593 500.2725 2293.5898
## 5 7515.866 15922.641 3254.754 1968.735 2110.682
## 6 2005.9071 18022.0020 7348.5205 41790.5160 44484.6330
## 7 502.2908 20148.4800 20126.8400 1599.8134 5405.7950
## 653.1834_2.294 675.1659_2.308 348.0769_2.315 446.0335_2.313 326.0892_2.313
## 2 500.0201 500.0201 1950.7544 500.0201 1772.1459
## 3 502.2117 502.2117 1720.4142 502.2117 1728.8490
## 4 500.2725 500.2725 1621.4033 500.2725 1122.4941
## 5 500.000 500.000 3782.358 2426.647 17648.123
## 6 156564.5200 148206.0600 98122.3400 67530.6900 843437.0600
## 7 502.2908 1043.6969 3945.9924 2553.4590 1233.1588
## 338.1178_2.307 165.0417_2.342 457.0963_2.367 227.9972_2.377 246.0109_2.385
## 2 500.0201 21899.0470 500.0201 65861.1600 1096.8365
## 3 502.2117 12035.6900 502.2117 75080.1950 2644.6387
## 4 500.2725 24832.4590 500.2725 49478.1050 1189.2017
## 5 500.000 10827.282 500.000 17093.960 2750.470
## 6 500.0225 14773.1360 2259.8994 27637.5080 66962.5860
## 7 502.2908 38866.5860 502.2908 32202.3570 2549.2446
## 206.9967_2.418 479.0459_2.443 233.0124_2.47 645.1901_2.522 309.0242_2.522
## 2 15948.3860 500.0201 33029.7300 500.0201 500.0201
## 3 34906.7540 502.2117 28601.2520 502.2117 1160.0740
## 4 16647.9120 500.2725 12021.5330 500.2725 1167.9474
## 5 29180.840 500.000 10164.411 500.000 1164.297
## 6 26154.9590 500.0225 5366.0240 500.0225 1743.6094
## 7 28900.7680 502.2908 24986.8610 502.2908 2152.3250
## 230.0129_2.513 230.0159_2.501 483.0154_2.51 232.0047_2.518 516.1022_2.528
## 2 18111.7770 16665.1700 1284.0045 1882.2661 1137.5221
## 3 18752.5780 17105.0920 502.2117 2045.0801 1284.2080
## 4 2599.2092 2599.2092 500.2725 500.2725 500.2725
## 5 31888.178 24080.053 500.000 2336.106 1134.390
## 6 791158.0000 38006.5980 47916.9340 39621.2150 12032.2110
## 7 3692.0410 2773.9204 1381.8585 1461.1941 1284.2013
## 359.0975_2.524 274.0027_2.51 565.137_2.579 218.1033_2.56 489.0936_2.593
## 2 1599.7126 5232.6070 500.0201 9406.8480 500.0201
## 3 1188.7185 47321.2970 502.2117 14377.9570 1512.1270
## 4 500.2725 4110.2764 500.2725 10466.6890 500.2725
## 5 1166.592 13986.703 500.000 8230.018 500.000
## 6 3931.9773 4022.9514 500.0225 10757.5460 500.0225
## 7 1577.5042 22292.0840 502.2908 8732.9380 1315.8228
## 422.0544_2.559 432.0832_2.56 579.0437_2.578 223.0583_2.562 611.0554_2.581
## 2 1104.2598 500.0201 1039.5365 500.0201 500.0201
## 3 502.2117 502.2117 502.2117 502.2117 502.2117
## 4 500.2725 500.2725 1002.3151 500.2725 1092.6956
## 5 500.000 500.000 500.000 500.000 500.000
## 6 500.0225 500.0225 500.0225 500.0225 500.0225
## 7 502.2908 502.2908 1737.7618 502.2908 502.2908
## 609.0591_2.577 449.0698_2.589 650.1898_2.57 364.0955_2.559 320.1087_2.579
## 2 500.0201 1219.1797 500.0201 500.0201 500.0201
## 3 502.2117 1021.5049 502.2117 502.2117 502.2117
## 4 500.2725 500.2725 500.2725 500.2725 500.2725
## 5 500.000 500.000 500.000 500.000 500.000
## 6 1051.0883 1125.1115 500.0225 500.0225 500.0225
## 7 502.2908 1406.8080 502.2908 502.2908 502.2908
## 494.0742_2.602 579.126_2.604 361.0218_2.595 295.03_2.565 293.0743_2.543
## 2 500.0201 500.0201 1896.5963 500.0201 1251.0277
## 3 502.2117 1187.2145 1686.0376 1244.3517 1513.0652
## 4 500.2725 500.2725 1616.0011 500.2725 1981.0028
## 5 500.000 500.000 1625.198 1469.206 1259.049
## 6 500.0225 1529.4370 6475.2954 4355.3706 2277.0650
## 7 502.2908 1628.0400 1148.9181 1191.4563 1544.7667
## 293.0331_2.576 484.0498_2.566 623.0319_2.6 204.9812_2.586 307.0111_2.574
## 2 1513.2408 1068.2694 500.0201 50909.4530 500.0201
## 3 2188.2178 1088.0094 1464.6678 26349.6070 502.2117
## 4 1896.8730 500.2725 1028.0994 76979.2500 1260.3247
## 5 3129.836 500.000 500.000 41582.258 1192.413
## 6 50455.0200 1007.6409 1067.2473 126316.2400 8508.9480
## 7 1738.1543 1098.5785 1018.5519 104325.9500 502.2908
## 328.9944_2.584 356.1002_2.592 168.0302_2.597 801.2049_2.656 389.1059_2.672
## 2 500.0201 1126.5315 17155.6300 500.0201 500.0201
## 3 1513.3253 1004.8297 22743.0780 502.2117 1421.5360
## 4 1029.7555 1128.5786 24585.0230 500.2725 500.2725
## 5 500.000 500.000 26314.129 500.000 500.000
## 6 2172.9480 38366.8950 19339.6860 500.0225 10951.8910
## 7 1161.0303 2596.6245 20257.0860 1234.3824 1916.8765
## 487.0759_2.632 675.1222_2.643 457.097_2.652 675.2009_2.65 509.0519_2.681
## 2 500.0201 500.0201 500.0201 500.0201 2563.2800
## 3 502.2117 1012.2609 502.2117 502.2117 3832.6733
## 4 500.2725 500.2725 500.2725 500.2725 2756.9650
## 5 500.000 500.000 500.000 500.000 5336.205
## 6 1562.7051 500.0225 2590.1562 1027.5782 1662.3877
## 7 502.2908 1045.9308 1177.7500 502.2908 2306.1938
## 590.1471_2.657 178.0524_2.721 216.9812_2.65 829.2715_2.727 323.0414_2.68
## 2 500.0201 430067.5000 29082.2290 3268.4067 1270.5984
## 3 502.2117 558012.4400 43089.9450 5805.1704 502.2117
## 4 500.2725 302084.4000 20813.8540 1082.7236 500.2725
## 5 500.000 445030.120 24046.232 500.000 500.000
## 6 500.0225 171995.9700 48939.3700 500.0225 5156.4673
## 7 1154.0962 386786.4700 50271.4700 12940.9280 1222.4187
## 835.2888_2.725 303.072_2.684 260.0239_2.686 659.1667_2.763 669.1448_2.74
## 2 4796.1810 12730.7710 1589.9888 5241.6860 8758.1660
## 3 7558.7420 17661.3380 1312.1364 7732.2590 16029.6280
## 4 1253.8003 12083.9960 1296.7530 1978.4995 4091.1430
## 5 500.000 21257.040 3538.354 3215.178 2959.554
## 6 1105.3215 25477.3500 54481.5200 500.0225 3764.2087
## 7 15031.0910 29635.9880 1088.1534 5833.2200 22590.6210
## 313.9951_2.734 596.1248_2.733 480.1382_2.735 194.0459_2.735 194.0495_2.735
## 2 5580.3890 1975.0679 8888.5960 47558.7270 47558.7270
## 3 10188.7040 2632.9190 19508.8670 119709.3600 119709.3600
## 4 9679.0870 1078.3878 8442.5200 90103.2500 7482.2227
## 5 11386.321 2541.959 7655.292 108583.836 9327.712
## 6 6570.5880 500.0225 7068.9760 63520.3870 63520.3870
## 7 21870.3790 2943.0490 97898.5100 392848.9400 27427.8930
## 411.0814_2.735 395.0857_2.735 379.0909_2.789 549.1963_2.753 744.2193_2.763
## 2 1161.5116 7264.7417 39631.1700 39381.4140 4912.6953
## 3 4568.2075 16883.5160 44951.2660 61347.6800 7099.3203
## 4 2500.2192 11030.8450 22708.0180 13445.1730 1960.9044
## 5 3053.327 18969.777 44086.992 10282.172 1940.490
## 6 2178.0005 4956.7812 9066.3300 12858.3090 1098.7122
## 7 25949.7870 32805.0080 26848.9550 124875.2660 11788.7470
## 263.1041_2.759 464.1437_2.772 264.1071_2.755 665.1832_2.777 178.0515_2.811
## 2 569173.9000 53509.5270 97138.4100 7740.3335 582890.0600
## 3 825874.3000 77648.0160 138249.4200 8456.4920 629197.4000
## 4 291803.0000 22589.6680 48649.5980 2622.5708 330607.7800
## 5 256603.900 29994.000 45349.050 4272.781 543115.250
## 6 292615.9400 16214.3530 50580.3670 1148.2830 216390.9500
## 7 1521374.0000 77026.5300 218575.6000 6747.9180 548982.2000
## 188.9864_2.765 829.2715_2.758 835.2887_2.754 195.0514_2.765 396.0904_2.751
## 2 125332.4600 3268.4067 4796.1810 9668.1520 2773.4705
## 3 171900.1900 5805.1704 7558.7420 16462.2850 4553.3335
## 4 126318.1640 1082.7236 1253.8003 44346.1640 3827.3340
## 5 221183.200 500.000 500.000 12144.354 4661.544
## 6 75570.3700 1149.6036 1260.7935 42334.4060 2355.9920
## 7 182080.3100 12940.9280 15966.0510 129361.8400 9247.4820
## 459.1629_2.778 391.1122_2.774 263.1397_2.777 383.0644_2.771 242.0128_2.785
## 2 1440.4685 500.0201 24785.2480 10508.1360 87696.1500
## 3 2860.3567 502.2117 36097.6100 10048.5370 39388.5860
## 4 3474.9812 3514.7710 11254.7500 6677.0615 4049.3271
## 5 500.000 500.000 9767.227 6365.724 3620.569
## 6 3436.1748 2802.4949 11711.8180 9871.0160 4309.5260
## 7 28042.4790 10796.5210 64490.0800 24387.0410 5961.0737
## 263.0272_2.81 931.1224_2.809 669.1447_2.763 551.0331_2.81 835.0489_2.8
## 2 18140.5400 1254.2596 8758.1660 1184.8718 500.0201
## 3 15501.4590 1108.3787 15741.4490 1241.0110 1436.2246
## 4 12893.2110 500.2725 4091.1430 1162.4291 500.2725
## 5 17381.934 1192.154 2959.554 1748.406 500.000
## 6 108861.8100 500.0225 3764.2087 1210.9774 1288.9585
## 7 12464.6220 1427.3160 22590.6210 1598.5969 1269.8384
## 246.9917_2.802 565.0065_2.811 263.0591_2.805 331.091_2.747 549.1196_2.793
## 2 27239.2660 1493.6395 1163.3062 56195.7400 1929.5126
## 3 98978.9000 502.2117 1078.2225 57986.1640 1893.3818
## 4 19829.8440 1257.8796 500.2725 38724.4730 1031.8339
## 5 33894.020 1155.785 1244.970 32944.367 1270.531
## 6 7881.4440 1449.9929 3014.2866 44828.7030 3602.1836
## 7 20859.1580 1317.6528 1540.5650 73977.1900 2059.1460
## 265.0184_2.828 468.0834_2.815 464.0663_2.822 659.1239_2.825 549.035_2.814
## 2 1264.2222 1004.6373 1094.8403 500.0201 1317.6860
## 3 1211.6644 502.2117 1611.5156 502.2117 1155.6483
## 4 1023.2070 500.2725 1514.5114 500.2725 500.2725
## 5 1147.350 1006.675 1764.189 500.000 500.000
## 6 6242.4033 7345.4976 3142.1477 500.0225 1219.5210
## 7 1282.2894 1068.5092 1294.5444 502.2908 1213.9509
## 645.1099_2.832 383.0324_2.787 473.0218_2.809 181.9917_2.823 549.1963_2.777
## 2 1395.7289 5912.8260 3270.0110 1184.9646 39381.4140
## 3 1463.8264 4728.0770 3020.3740 1102.5000 61347.6800
## 4 500.2725 4706.5728 2659.0908 1683.8644 13445.1730
## 5 1050.546 3316.681 2979.912 4367.650 10282.172
## 6 500.0225 8058.6973 4164.3700 137960.9500 13319.2690
## 7 1493.8925 5256.6304 2292.6611 3359.2764 124875.2660
## 464.1437_2.808 419.1155_2.861 610.164_2.859 896.2543_2.86 219.0524_2.85
## 2 60517.5800 500.0201 5761.9770 1530.5090 500.0201
## 3 77648.0160 502.2117 6249.2960 1049.4247 502.2117
## 4 22589.6680 500.2725 2146.5510 500.2725 500.2725
## 5 29994.000 500.000 500.000 500.000 13221.704
## 6 18029.0120 5512.6924 2944.6382 500.0225 500.0225
## 7 80643.5700 502.2908 21186.6350 4827.6700 16056.6080
## 505.1427_2.841 580.1297_2.848 324.0723_2.857 665.1833_2.829 219.0509_2.854
## 2 1196.0145 5893.4844 30983.6210 8325.7640 500.0201
## 3 1200.0968 4994.8843 27085.7910 8456.4920 502.2117
## 4 500.2725 2140.1482 25058.8050 2833.0500 1030.7941
## 5 2385.180 4268.077 11818.713 4272.781 15151.294
## 6 1112.1592 1546.4408 31264.0410 1989.6998 1124.0906
## 7 38062.6170 2892.5364 98016.6950 6747.9180 232285.7000
## 659.1666_2.837 263.1041_2.796 645.1893_2.881 560.1364_2.889 441.0612_2.876
## 2 6892.9250 552014.1000 1417.7347 500.0201 11581.2680
## 3 7546.5280 790106.5000 1507.5568 1010.2306 9240.3890
## 4 1986.5365 291803.0000 500.2725 500.2725 5040.6350
## 5 3215.178 256603.900 500.000 500.000 9564.785
## 6 1191.7554 295047.2800 1813.0662 1330.9205 4080.5857
## 7 6881.4030 1381773.8000 1361.5044 1112.5435 5153.3027
## 574.1142_2.883 343.0669_2.881 178.0513_2.879 379.0909_2.88 241.1193_2.896
## 2 3906.2615 25783.2930 712494.5600 56203.8750 19704.2290
## 3 4457.3070 11092.4710 623981.8000 42185.8700 43030.2930
## 4 1635.8798 12100.0500 352942.8800 21750.9430 36307.1100
## 5 3542.160 3660.505 543115.250 44153.900 9643.351
## 6 1020.1672 2780.4540 253609.4000 12113.6640 17985.6040
## 7 2298.9038 18084.5720 580858.1000 32058.9700 23183.8320
## 203.0021_2.888 246.0383_2.873 574.1523_2.913 134.0611_2.89 370.0781_2.895
## 2 16123.0040 72523.9400 500.0201 27422.1660 1753.0948
## 3 48660.6400 60718.7230 502.2117 23332.5820 1532.4760
## 4 18991.7100 55614.6000 500.2725 12033.9730 2974.4856
## 5 28931.715 83623.164 500.000 20631.459 1742.336
## 6 16632.7000 42570.6330 1073.2687 8854.5240 2740.1174
## 7 19111.6110 41042.7340 1010.2699 20185.7710 15739.5600
## 468.0438_2.896 277.0007_2.942 464.1436_2.856 432.1639_2.932 395.0857_2.92
## 2 1630.9438 2764.4556 60517.5800 500.0201 4678.2500
## 3 1174.2214 1816.1577 62852.1900 502.2117 19229.0000
## 4 1794.5576 3747.0417 17987.0980 500.2725 4045.2932
## 5 1024.719 2219.419 29994.000 500.000 8879.860
## 6 1138.2505 5460.6675 18029.0120 2839.2302 2433.5217
## 7 2265.8425 4001.6720 80643.5700 502.2908 13934.5650
## 194.0459_2.921 373.1067_2.937 659.2045_2.918 389.1005_2.94 388.047_2.937
## 2 24421.2300 5205.5020 500.0201 500.0201 21387.0370
## 3 135220.5300 28267.5980 1275.6581 7641.4185 36570.9600
## 4 28190.6820 3377.2227 500.2725 1311.1658 11045.6390
## 5 42752.773 9139.062 500.000 1345.664 11872.902
## 6 39412.0660 11700.8460 1064.9032 9700.9730 11901.3420
## 7 146909.3900 29116.9380 1136.2034 11689.8130 25565.7270
## 422.1084_2.915 473.0996_2.932 428.0941_2.98 157.0506_2.959 357.0826_2.931
## 2 500.0201 15568.2010 500.0201 23276.5700 4140.4717
## 3 502.2117 38337.5000 1212.6360 23729.4410 1923.6031
## 4 500.2725 6745.8630 500.2725 40769.4960 3403.0366
## 5 500.000 4761.560 500.000 14662.697 2746.002
## 6 1230.8770 9643.7780 3624.5490 38093.6640 3540.5752
## 7 1393.5662 37457.2800 1315.9333 26698.3830 20611.3070
## 459.0843_2.964 263.1054_2.91 623.1618_2.946 374.0316_2.965 227.0628_2.987
## 2 7907.9385 522071.9700 1284.4814 11341.1540 1012.7760
## 3 16352.2910 583875.5600 1028.4437 18363.0530 502.2117
## 4 6574.0000 226123.2300 500.2725 12535.9100 500.2725
## 5 3871.335 204995.860 500.000 11371.824 500.000
## 6 2946.0488 295047.2800 1066.5320 3823.3472 10034.8740
## 7 16488.6600 1323426.1000 1008.9670 16596.3320 502.2908
## 477.1039_2.949 139.0401_3.003 227.0551_2.986 609.1484_2.937 322.1104_2.943
## 2 500.0201 1051.8927 1485.8943 2539.2603 500.0201
## 3 1285.8231 1024.3806 1217.1273 1957.0605 502.2117
## 4 500.2725 500.2725 1569.8837 500.2725 1002.4456
## 5 500.000 500.000 1152.879 1008.658 500.000
## 6 1268.8782 4151.3193 14865.2890 500.0225 500.0225
## 7 1123.1480 1233.3864 502.2908 3621.3490 502.2908
## 1093.9937_2.988 785.2097_2.974 336.1218_3.046 563.1044_2.949 487.0726_2.982
## 2 14059.8980 500.0201 1228.1272 1366.6039 1291.2161
## 3 19792.4380 502.2117 1322.5994 1505.8705 502.2117
## 4 24328.4380 500.2725 1852.0878 1219.2864 1037.9515
## 5 21201.113 500.000 1044.224 1060.485 1200.075
## 6 10119.5340 500.0225 1324.4464 1141.1499 1299.7789
## 7 19701.6900 1508.3335 1304.3164 2479.1418 1657.1627
## 389.1043_2.952 173.0001_2.997 577.1184_2.969 771.1944_2.999 368.9723_3.004
## 2 500.0201 15839.0090 500.0201 500.0201 5975.8730
## 3 3734.0420 27973.3320 1818.1772 502.2117 26272.9840
## 4 1241.3461 27233.0210 500.2725 500.2725 23327.0820
## 5 500.000 18762.623 500.000 500.000 12524.897
## 6 9700.9730 10526.3880 500.0225 500.0225 4273.0464
## 7 6027.4710 27079.7730 1620.6034 502.2908 18753.4000
## 172.9915_3.001 359.1353_3.002 382.9875_3.002 875.1494_2.993 555.0787_2.996
## 2 228495.1700 2235.3823 12367.2190 500.0201 500.0201
## 3 543342.4400 2762.0930 58994.6600 502.2117 502.2117
## 4 461902.0300 3007.4146 22580.5140 500.2725 500.2725
## 5 275754.160 2034.514 12267.216 500.000 1000.227
## 6 149352.1000 4497.6455 11250.2930 500.0225 1806.5128
## 7 432861.4400 15491.3340 37467.4920 1036.0077 1070.4640
## 277.0329_2.993 855.1197_2.99 645.1887_2.968 360.1388_3.006 224.0561_3.018
## 2 1357.0261 500.0201 500.0201 1218.9586 30314.6930
## 3 1085.4979 502.2117 502.2117 1373.2695 7335.3496
## 4 2463.6060 500.2725 500.2725 500.2725 8555.2150
## 5 1895.586 500.000 500.000 500.000 8715.640
## 6 6097.4030 500.0225 2040.2936 1191.8896 8576.3620
## 7 3241.9102 502.2908 502.2908 2906.1467 16478.8400
## 1117.2561_3.018 861.1356_3.001 560.1365_2.971 800.2743_3.027 181.0504_3.017
## 2 500.0201 500.0201 500.0201 500.0201 24167.7680
## 3 502.2117 502.2117 502.2117 502.2117 123787.5900
## 4 500.2725 500.2725 500.2725 1187.7285 38108.6520
## 5 500.000 500.000 500.000 500.000 29161.830
## 6 500.0225 500.0225 2716.6870 500.0225 27173.9920
## 7 502.2908 502.2908 502.2908 502.2908 176814.8800
## 741.1857_3.018 359.0958_2.977 1123.2728_3.021 457.0623_2.989 741.1856_3.019
## 2 500.0201 1950.8844 500.0201 1511.4958 500.0201
## 3 502.2117 502.2117 502.2117 2918.6355 502.2117
## 4 500.2725 1433.5070 500.2725 500.2725 500.2725
## 5 500.000 1120.713 500.000 1434.815 500.000
## 6 1464.4463 99050.0550 500.0225 8062.7080 1464.4463
## 7 502.2908 2729.3610 502.2908 2425.1157 502.2908
## 479.0465_3.005 547.1102_3.005 418.1842_3.015 569.0928_2.988 427.0844_2.991
## 2 1015.8890 1115.2130 500.0201 500.0201 500.0201
## 3 1293.2338 1254.8406 1044.8848 1342.1074 1358.4607
## 4 500.2725 500.2725 500.2725 500.2725 500.2725
## 5 500.000 1132.767 1129.500 1024.919 1216.882
## 6 9337.2300 1172.8724 18896.3100 4701.3647 15889.6640
## 7 1005.1578 502.2908 502.2908 1022.4750 1556.0026
## 951.181_3.008 364.0972_3.027 755.2012_3.03 189.0027_3.097 1131.2716_3.026
## 2 500.0201 500.0201 500.0201 34493.9020 500.0201
## 3 502.2117 502.2117 502.2117 77083.4900 502.2117
## 4 500.2725 500.2725 500.2725 26709.7700 500.2725
## 5 500.000 500.000 500.000 19874.867 500.000
## 6 500.0225 500.0225 500.0225 33752.4960 500.0225
## 7 502.2908 502.2908 502.2908 58609.1200 502.2908
## 945.1645_3.008 605.1126_3.023 379.0338_3.038 1137.288_3.026 230.0128_3.042
## 2 500.0201 3806.9210 5589.8840 500.0201 42695.7200
## 3 502.2117 2101.3250 12932.4030 502.2117 164720.3000
## 4 500.2725 500.2725 5847.4830 500.2725 9055.0930
## 5 500.000 500.000 5296.986 500.000 25247.043
## 6 500.0225 500.0225 3140.2322 500.0225 6230.3080
## 7 502.2908 1248.9188 7005.9346 502.2908 9821.0150
## 814.2904_3.028 336.1214_3.081 432.2027_3.06 828.3048_3.061 500.1902_3.066
## 2 500.0201 1702.6837 1075.9193 500.0201 500.0201
## 3 502.2117 1711.1191 502.2117 502.2117 502.2117
## 4 500.2725 1852.0878 500.2725 500.2725 500.2725
## 5 500.000 1044.224 500.000 500.000 500.000
## 6 500.0225 1362.4879 21346.4320 500.0225 6366.7334
## 7 502.2908 1304.3164 502.2908 502.2908 502.2908
## 361.1499_3.066 109.0294_3.11 188.0103_3.118 330.0287_3.095 337.056_3.104
## 2 1650.4202 500.0201 64036.3600 12306.4730 43507.0940
## 3 7590.4560 1165.1726 144177.0800 5873.3220 1467.1447
## 4 3670.9233 1267.8936 44057.8200 4786.0493 1786.5221
## 5 2687.157 500.000 29924.877 18903.006 3224.266
## 6 8064.4507 1546.4446 62198.7580 13038.8310 1684.1503
## 7 12763.8090 502.2908 104678.0600 5482.9460 9325.2530
## 197.0442_3.134 375.1278_3.109 979.2124_3.136 1108.0095_3.117 397.0032_3.133
## 2 1582.0250 2554.6326 500.0201 39322.9700 39249.4600
## 3 1201.0673 9047.2280 502.2117 46984.0350 135508.2800
## 4 2969.0798 5938.3530 500.2725 33850.3120 21442.4120
## 5 1757.130 1790.583 500.000 35272.830 13288.415
## 6 2792.0032 6773.0890 500.0225 37220.9200 38784.6100
## 7 1808.8665 21871.0620 502.2908 49979.1130 84667.9800
## 583.1092_3.147 889.1651_3.147 187.0383_3.122 187.0075_3.121 973.198_3.133
## 2 1121.1277 500.0201 28279.0040 725395.7000 500.0201
## 3 1553.4073 502.2117 68913.6400 1835242.2000 502.2117
## 4 500.2725 500.2725 16783.2970 453787.2000 500.2725
## 5 500.000 500.000 11901.387 327898.030 500.000
## 6 3172.1243 500.0225 25334.3160 657575.0000 500.0225
## 7 1627.6866 502.2908 49611.6800 1310167.2000 502.2908
## 313.0569_3.13 189.0026_3.12 373.0764_3.153 883.1498_3.145 209.0452_3.159
## 2 500.0201 40609.0900 1451.3241 500.0201 500.0201
## 3 502.2117 77083.4900 502.2117 502.2117 502.2117
## 4 1100.7655 26800.7100 500.2725 500.2725 500.2725
## 5 500.000 19874.867 500.000 500.000 500.000
## 6 3479.0872 36243.5400 1334.4143 500.0225 1454.0785
## 7 3640.0960 58609.1200 1544.6494 502.2908 1420.8890
## 254.9941_3.14 211.0611_3.158 747.2342_3.15 413.1436_3.2 493.0653_3.147
## 2 30327.8930 3144.8100 500.0201 1291.3829 1505.3153
## 3 49599.2850 8091.9644 502.2117 1676.5596 1463.2155
## 4 22590.5270 3917.6372 500.2725 1659.1958 1354.6180
## 5 17440.748 4739.452 500.000 1180.482 1028.109
## 6 29116.4120 5361.1714 500.0225 1563.1838 3342.8160
## 7 38267.1200 3043.8208 502.2908 1831.2222 1593.8037
## 276.0009_3.151 769.2171_3.155 373.1513_3.174 471.0817_3.145 397.0032_3.134
## 2 500.0201 500.0201 1431.4451 1211.0331 39249.4600
## 3 502.2117 502.2117 502.2117 1474.2480 135508.2800
## 4 500.2725 500.2725 1178.6276 1141.4017 21442.4120
## 5 11798.137 500.000 1601.252 1174.738 13288.415
## 6 45853.5740 500.0225 1393.0183 1658.3204 38784.6100
## 7 502.2908 502.2908 1581.8109 1496.6775 84667.9800
## 218.9964_3.175 373.114_3.2 175.0611_3.177 230.9968_3.178 429.0831_3.189
## 2 6321.5107 2925.5212 10914.0240 13176.9260 28858.9450
## 3 24121.7070 5051.8594 9954.0970 25346.6900 18626.2900
## 4 27496.1520 4485.0410 18593.0740 13596.5730 1156.7950
## 5 25566.215 2577.343 26890.793 61188.600 500.000
## 6 5877.7227 27763.6860 5454.3076 23754.1460 500.0225
## 7 17027.7710 10832.5160 7648.3100 38262.9450 502.2908
## 311.0752_3.182 785.1841_3.18 441.1026_3.212 1124.0041_3.197 599.1056_3.199
## 2 1799.5591 500.0201 1254.1736 1171.2819 500.0201
## 3 2623.8342 502.2117 502.2117 1529.9209 502.2117
## 4 1156.8150 500.2725 500.2725 2603.4631 500.2725
## 5 2230.256 500.000 500.000 3558.246 500.000
## 6 59223.1900 500.0225 5217.1530 1624.3170 1091.4772
## 7 5574.2534 502.2908 502.2908 1314.7184 502.2908
## 493.0642_3.179 428.9933_3.184 203.0048_3.202 412.9977_3.204 204.9979_3.197
## 2 1495.0741 1401.9183 27376.5040 4577.0903 1948.8909
## 3 1393.2332 4389.1313 43821.9450 11395.8790 3473.2046
## 4 1296.8182 2192.7295 44778.0740 4682.3500 3641.9128
## 5 1029.883 1831.237 70541.990 6593.104 5097.104
## 6 3342.8160 1069.1965 41308.2580 8312.1045 3152.6338
## 7 1086.0625 2056.2864 34643.6950 8231.4250 3754.8735
## 203.002_3.205 303.0178_3.212 370.097_3.208 448.1959_3.232 1108.0095_3.194
## 2 27376.5040 4841.2476 7298.0474 500.0201 39322.9700
## 3 43821.9450 8432.9010 11697.9730 502.2117 40273.1680
## 4 44778.0740 5308.7080 4489.0293 500.2725 30145.4530
## 5 70541.990 6000.432 3371.135 500.000 32093.627
## 6 41308.2580 29464.3950 5011.5527 4333.9290 34589.2800
## 7 34643.6950 16405.0490 15327.7480 502.2908 42463.1400
## 429.083_3.201 593.1477_3.228 403.1971_3.26 605.1462_3.238 261.0071_3.241
## 2 28858.9450 500.0201 4809.0405 500.0201 1312.1093
## 3 18626.2900 502.2117 5487.4530 502.2117 6019.4976
## 4 1156.7950 500.2725 1935.7369 500.2725 2738.3020
## 5 500.000 500.000 13184.255 500.000 9583.148
## 6 500.0225 1024.6782 3571.7175 500.0225 2233.9287
## 7 502.2908 502.2908 2509.0200 1458.7856 28961.7990
## 209.0452_3.24 368.0971_3.297 509.0409_3.291 1004.2068_3.286 675.9686_3.302
## 2 500.0201 1528.0566 16441.8030 500.0201 1067.3505
## 3 502.2117 1605.1406 23053.1660 502.2117 6176.8887
## 4 500.2725 1167.4445 1587.6748 500.2725 1225.1904
## 5 500.000 500.000 500.000 500.000 1599.655
## 6 1335.0479 1091.7689 1391.2784 500.0225 1230.1396
## 7 1420.8890 1252.5865 1236.9949 502.2908 10873.5610
## 608.1059_3.3 423.9954_3.299 681.985_3.302 212.0348_3.301 212.0109_3.298
## 2 500.0201 4184.5684 1686.9532 19439.3950 29946.0740
## 3 1257.9000 14292.3800 10467.9300 53517.1100 35281.3870
## 4 500.2725 3379.8555 2960.2507 25333.8930 35919.4400
## 5 500.000 1659.044 500.000 9232.063 21052.742
## 6 2132.1924 5947.7715 2097.8850 20752.4880 35933.5350
## 7 1638.5148 12934.1770 26128.1840 84059.9140 2170537.0000
## 1133.0047_3.302 258.9926_3.301 421.9984_3.301 448.9914_3.302 279.9896_3.301
## 2 12797.5350 4464.9920 35450.4900 4404.7970 35932.0400
## 3 14018.1910 20303.9380 123168.1250 12268.9200 51047.7100
## 4 23355.4160 2779.1426 27037.9730 4722.0674 43687.1130
## 5 13411.941 16626.994 10325.753 1671.214 27310.984
## 6 14721.5450 10513.3630 45261.7340 4655.2334 44516.5080
## 7 25735.3750 11031.1250 118986.4400 21651.2170 68140.0200
## 459.0926_3.306 336.0718_3.292 213.9981_3.303 212.0027_3.304 446.9937_3.304
## 2 17902.4410 16148.0720 27911.1900 515011.8000 28600.3030
## 3 14738.7490 9804.8060 63811.2150 1357483.8000 113461.4400
## 4 1076.4628 7657.4443 30817.9260 618903.8000 42696.1520
## 5 500.000 2649.646 16270.612 271142.440 10988.386
## 6 500.0225 11947.9290 37721.7340 624528.3000 39930.9500
## 7 1484.4175 4903.3760 90533.0000 2170537.0000 204963.8300
## 572.1094_3.326 429.0423_3.352 215.0021_3.307 594.0962_3.363 283.0822_3.328
## 2 500.0201 2229.6282 30515.8340 4259.0854 40946.7150
## 3 502.2117 1990.6552 44670.7000 2377.0000 59168.2000
## 4 1203.5193 1930.7524 8639.1710 1370.1451 34351.0040
## 5 500.000 1217.086 92194.445 1024.509 14026.344
## 6 500.0225 2240.3625 31006.0040 1611.0526 47110.3630
## 7 502.2908 3608.4700 17411.1020 4252.1885 85490.8050
## 569.0956_3.38 245.0124_3.342 755.1981_3.389 273.0074_3.349 347.0881_3.328
## 2 1287.2800 8036.6990 500.0201 21716.8000 500.0201
## 3 1274.6588 56800.0400 502.2117 58623.1330 502.2117
## 4 500.2725 15923.6050 500.2725 32809.4300 1064.0841
## 5 1122.002 15662.349 500.000 43919.934 500.000
## 6 1236.8182 9428.4320 500.0225 47351.6560 1424.5940
## 7 1189.5416 65532.4920 1470.4814 44389.1700 2911.9338
## 243.1349_3.353 237.0356_3.369 574.1534_3.396 457.0651_3.354 404.0395_3.379
## 2 37066.1950 500.0201 500.0201 500.0201 1617.2145
## 3 15518.8300 502.2117 502.2117 502.2117 1894.1229
## 4 28669.3710 500.2725 1050.2803 1061.5360 1597.4543
## 5 5885.612 1284.558 500.000 500.000 1430.279
## 6 21825.2830 1065.7505 1038.6943 1686.2825 1847.7064
## 7 9667.5625 1024.9375 1054.7756 1319.7858 2474.0007
## 350.088_3.366 388.047_3.398 308.0774_3.378 345.1554_3.382 357.0825_3.399
## 2 28473.0350 23033.5040 8928.0330 3215.5464 1369.4939
## 3 34917.1640 37494.9700 2757.6985 14827.9990 1168.5048
## 4 33827.9920 13517.0660 3893.1592 7265.2607 1548.8846
## 5 7937.236 13641.189 1195.560 3706.526 2546.284
## 6 19562.9570 11891.3150 4109.6245 12293.0205 500.0225
## 7 16392.7440 23257.7250 22572.1780 20402.7400 22256.2710
## 283.0822_3.343 381.0802_3.385 347.17_3.394 645.1896_3.451 372.1099_3.404
## 2 40946.7150 1248.5895 1777.5074 2839.9000 4393.8200
## 3 59168.2000 1297.4020 1619.6880 3352.0417 2316.0000
## 4 34351.0040 1594.8368 8759.6330 1625.7839 22392.5060
## 5 14026.344 1237.889 1235.619 1933.333 9403.422
## 6 47110.3630 2476.5938 16943.1210 1261.0593 1336.3472
## 7 85490.8050 1424.4496 7108.1370 2030.4913 1796.5156
## 380.0942_3.483 359.1347_3.481 178.0557_3.444 240.9816_3.419 393.0614_3.427
## 2 32171.9690 6637.6074 33387.3160 76310.1640 18147.8160
## 3 23183.5880 10761.7310 35296.3200 2467.5962 18278.2870
## 4 22360.6150 9616.3010 42495.6370 6403.5713 20145.7000
## 5 32012.945 7535.427 1492137.800 27549.479 17839.291
## 6 8855.1160 10072.2500 30864.3000 4978.0310 27679.4730
## 7 16139.8270 31457.8980 37236.3700 13784.1790 23142.6820
## 659.2057_3.452 275.0224_3.427 331.091_3.433 297.0036_3.423 336.0706_3.376
## 2 1011.9239 1420.3838 60085.2200 1354.6753 2559.7950
## 3 1295.6661 7311.3340 59490.2200 1590.6173 1734.8120
## 4 1025.2408 1792.4160 64656.6560 1537.6403 1323.7981
## 5 500.000 2959.763 46114.137 1095.457 500.000
## 6 1459.6862 2168.9020 90255.9840 1227.8118 500.0225
## 7 1565.1840 1959.2510 85001.9500 1731.3763 1036.4287
## 361.1502_3.49 569.1751_3.441 338.0632_3.433 246.0383_3.487 321.0622_3.448
## 2 2958.1145 6952.4550 1654.4623 92775.0700 29864.2000
## 3 11504.6930 11497.2870 502.2117 75916.8300 30422.8440
## 4 5831.1460 3894.2944 500.2725 97662.7300 31541.7030
## 5 3116.306 1575.881 500.000 113493.880 25448.340
## 6 5918.6294 6497.2915 500.0225 72250.6800 30603.7870
## 7 8466.9720 18514.0700 502.2908 59849.7340 31963.0300
## 636.1806_3.455 264.1168_3.46 611.1667_3.462 526.114_3.463 675.1598_3.47
## 2 6597.0000 18984.1230 11994.7705 25346.2580 1367.0046
## 3 6171.3887 209550.1400 13618.3140 22599.6450 1885.2966
## 4 3551.7068 12719.3780 8018.0566 18018.1400 1052.2578
## 5 1692.240 11339.950 5001.151 17458.889 2040.139
## 6 3185.5290 15336.2070 11976.2890 13655.1570 1696.0833
## 7 5005.0073 34772.8550 18729.1880 18866.6130 1665.9763
## 759.1899_3.464 663.1291_3.465 473.0995_3.464 1115.3646_3.466 835.2891_3.466
## 2 6770.3710 17872.6270 26638.6520 9007.8600 30294.4280
## 3 17925.9960 25745.9630 71092.4900 14915.7900 54536.9500
## 4 3023.3018 8960.3900 11244.1830 1858.0869 9193.0830
## 5 2437.424 5286.119 9080.140 500.000 3969.529
## 6 3502.1830 10144.6280 23421.8380 2555.3733 12742.8410
## 7 17017.3070 31538.5230 57073.7500 40242.0940 93430.3700
## 263.141_3.465 669.1453_3.465 565.1701_3.465 1121.3811_3.466 813.2953_3.466
## 2 91165.7200 31356.9940 8595.8220 6712.3003 6448.3354
## 3 104570.1300 34249.3120 12769.9330 9404.1880 11707.7450
## 4 26066.0000 13491.6980 3599.4756 1444.8982 2950.2415
## 5 32622.402 10226.504 2471.274 500.000 1094.941
## 6 47276.6200 18428.2910 5163.5684 1624.0774 2361.4160
## 7 129550.0900 51002.4920 19104.1900 21716.1460 24760.5920
## 361.0713_3.465 829.2722_3.466 549.1965_3.467 971.2036_3.465 263.1046_3.466
## 2 27392.8800 24744.9510 202512.7200 9367.9380 2398278.2000
## 3 32319.6050 38729.4020 262769.6600 12603.4460 3079720.0000
## 4 14878.2650 5787.4517 80314.7700 4049.7893 984361.2000
## 5 11316.180 5231.621 48212.836 2011.288 766227.750
## 6 15289.3930 7975.6540 92371.7200 4322.8286 1079254.1000
## 7 38964.1800 74986.2200 437470.5000 16567.9670 4669233.0000
## 383.053_3.466 527.2139_3.467 728.2422_3.466 949.2215_3.466 145.0619_3.467
## 2 86987.2100 20327.6540 8234.7130 7274.3540 64166.7400
## 3 101857.5300 35878.9770 11448.2260 12761.2180 78737.6800
## 4 64309.7150 6010.0723 4072.0950 3739.2588 33152.9840
## 5 55675.140 4094.643 3181.435 1704.055 25409.906
## 6 69386.6250 8192.3100 2556.0989 2641.2551 33648.3750
## 7 119541.6300 57606.1300 11523.1130 22226.4340 127784.8800
## 744.2195_3.468 1030.3123_3.466 750.2363_3.468 480.1372_3.468 1257.2949_3.465
## 2 27855.5530 12820.2660 29474.2300 1708.6261 4634.3050
## 3 34246.8750 14998.2000 30712.8360 2180.9597 4342.8440
## 4 12259.0150 3155.5745 12023.7970 2122.2717 1329.2925
## 5 10039.240 1941.837 10111.634 1813.109 500.000
## 6 7884.7427 2729.7888 9491.5340 2119.3398 1443.2474
## 7 34506.3670 16687.0300 26660.2230 3721.5520 9309.0120
## 674.1395_3.477 643.1894_3.473 230.9969_3.457 945.2595_3.469 464.1438_3.474
## 2 4456.7144 8002.7534 2387.2107 14134.8770 191703.7300
## 3 7142.2456 8498.5340 5497.5380 14474.3740 181057.9700
## 4 3665.4540 4157.4053 7491.0020 5632.1123 116001.0860
## 5 2976.649 4629.730 37576.363 5843.373 117608.930
## 6 3044.6480 2571.2979 5612.9424 2455.5017 77506.0100
## 7 5177.9090 6552.0117 16211.3970 12756.3920 162613.1200
## 659.1667_3.474 665.1835_3.474 369.0829_3.476 388.047_3.472 178.0818_3.476
## 2 26325.2500 28839.4410 3680.2852 23033.5040 71185.5550
## 3 26661.6880 22829.7620 5441.3280 45957.4060 67737.3300
## 4 11779.7680 16705.2100 3148.8413 16806.2660 42114.6880
## 5 16950.727 19587.285 5100.924 14923.624 58070.164
## 6 5978.7695 6944.2935 6644.0894 15894.6090 21287.2090
## 7 19562.0410 19230.8380 7792.7715 28514.1520 48103.1560
## 357.1107_3.472 692.1776_3.461 860.2066_3.476 134.0612_3.478 178.0515_3.479
## 2 14011.9250 500.0201 10398.2710 48428.3750 1932230.2000
## 3 13775.3510 502.2117 8563.5970 43549.1800 1811344.0000
## 4 6658.9956 1036.9456 4538.2030 32288.1200 1065876.6000
## 5 12946.002 500.000 7695.449 44112.535 1492137.800
## 6 5244.0557 500.0225 1632.3392 19090.2420 576666.1000
## 7 10987.5810 502.2908 4974.2500 37712.8950 1247351.6000
## 558.136_3.478 441.0613_3.482 574.1139_3.482 236.0095_3.482 775.1556_3.475
## 2 5825.8260 21828.8650 18109.1230 39066.0080 5742.7217
## 3 3876.0693 15824.3530 13169.5880 37001.3000 4708.5713
## 4 2554.7070 16511.3700 8449.4000 42179.5900 2568.1091
## 5 4499.181 23779.107 13967.390 49828.348 4879.240
## 6 1612.5391 7390.1455 2859.6802 27509.8900 1227.6125
## 7 2306.6038 10609.4880 5463.6865 25460.7930 2693.2432
## 580.1303_3.48 379.091_3.484 590.1068_3.469 607.1267_3.483 371.0978_3.481
## 2 18088.8440 141366.0200 1108.0483 1024.0109 5079.8354
## 3 15281.7350 119121.7500 1563.5682 1148.3680 5834.8325
## 4 11567.5510 92462.8800 500.2725 1131.4443 7468.1660
## 5 21576.766 158943.120 1351.796 1201.111 2381.384
## 6 3191.2610 39452.0040 500.0225 1003.4628 3133.8696
## 7 6442.8700 70578.8200 1194.7349 1051.4238 5505.6050
## 437.0497_3.488 393.0776_3.464 298.0002_3.49 395.091_3.521 178.0526_3.481
## 2 8622.9920 3256.7703 47037.9730 2911.5117 1932230.2000
## 3 7041.3765 2940.2522 42788.4570 2672.5693 1811344.0000
## 4 8870.4910 2948.6920 43298.9380 3203.7642 1065876.6000
## 5 11053.334 2463.968 59809.562 2305.755 1492137.800
## 6 4157.9785 2987.5312 29050.8750 2508.1930 576666.1000
## 7 3944.3708 3215.4324 30930.8100 5999.9756 1247351.6000
## 246.0383_3.488 299.0779_3.509 412.1044_3.523 640.1347_3.515 359.1345_3.504
## 2 92775.0700 2514.0671 1316.6816 1059.8517 7766.5327
## 3 75916.8300 5905.4960 1395.9752 502.2117 10761.7310
## 4 97662.7300 4428.6410 1712.9783 500.2725 9616.3010
## 5 113493.880 2112.329 1042.024 500.000 7535.427
## 6 72250.6800 3174.4722 1563.0499 500.0225 10072.2500
## 7 59849.7340 4038.0550 1918.7172 502.2908 31457.8980
## 396.1297_3.506 242.0128_3.535 399.1658_3.513 432.1631_3.52 666.2108_3.475
## 2 1237.8220 15092.3380 14871.4610 500.0201 1416.0035
## 3 1081.2500 7215.1846 29974.5700 1380.7079 1618.9263
## 4 500.2725 4597.5000 5545.9253 500.2725 1036.3748
## 5 500.000 3201.710 20128.006 500.000 500.000
## 6 500.0225 4437.1504 7750.1367 5089.0770 500.0225
## 7 502.2908 2932.0889 14633.6920 502.2908 1110.3527
## 251.0128_3.524 404.0405_3.511 279.0487_3.569 241.1189_3.536 150.0562_3.542
## 2 1514.0778 1378.1066 1180.4450 39766.2970 1171.8206
## 3 1187.0214 2491.6528 502.2117 95559.9600 1434.4391
## 4 1249.9388 1597.4543 1334.9155 77710.1250 500.2725
## 5 2018.757 1505.133 1316.928 11695.774 1310.916
## 6 2546.0176 1509.6803 3898.3310 35093.3500 500.0225
## 7 8027.5960 2086.8123 1216.1870 43619.8550 2278.5050
## 287.0227_3.548 507.0806_3.582 565.0578_3.576 411.0792_3.55 489.2111_3.578
## 2 41345.4730 1448.2427 1177.1458 1696.9452 500.0201
## 3 15155.2930 502.2117 1004.4235 1300.8954 6157.4854
## 4 27982.4430 500.2725 1231.4302 1417.0740 1008.0990
## 5 3729.891 500.000 500.000 1033.423 500.000
## 6 2974.8430 500.0225 500.0225 1345.0096 500.0225
## 7 70927.5200 502.2908 1217.3944 1259.5208 502.2908
## 337.1469_3.591 194.0459_3.542 913.214_3.531 338.0879_3.557 622.0984_3.522
## 2 500.0201 4766.7570 1478.5022 23926.6350 500.0201
## 3 502.2117 8600.8620 1213.0704 6397.5176 502.2117
## 4 1047.5446 6345.9640 500.2725 2405.2570 500.2725
## 5 500.000 7711.624 1249.429 500.000 500.000
## 6 1246.6431 10094.8920 500.0225 2597.2285 500.0225
## 7 502.2908 18734.6290 502.2908 2083.7493 502.2908
## 559.0435_3.584 230.0128_3.569 331.0121_3.56 1141.2436_3.619 428.0976_3.601
## 2 1786.7450 126730.4700 1126.1914 500.0201 500.0201
## 3 1297.4121 38399.6400 1699.8720 502.2117 502.2117
## 4 500.2725 4776.5780 1268.7216 500.2725 500.2725
## 5 1050.114 1810.725 1482.222 500.000 1198.921
## 6 500.0225 4558.3430 3006.1926 500.0225 500.0225
## 7 502.2908 7235.9310 1488.0045 502.2908 502.2908
## 461.0863_3.571 891.2154_3.643 364.0958_3.594 680.165_3.608 255.1349_3.614
## 2 1315.3243 500.0201 500.0201 500.0201 22303.9140
## 3 1294.7432 502.2117 502.2117 502.2117 46002.7200
## 4 1070.9199 500.2725 500.2725 500.2725 36518.0000
## 5 1135.873 500.000 500.000 500.000 8328.170
## 6 1177.4474 500.0225 500.0225 500.0225 21621.3650
## 7 1189.2926 502.2908 1071.6423 502.2908 11909.5910
## 495.2229_3.594 571.1469_3.618 556.1624_3.635 525.0334_3.606 322.1105_3.605
## 2 500.0201 500.0201 500.0201 12501.2710 500.0201
## 3 502.2117 1323.5890 502.2117 7867.9565 502.2117
## 4 1286.0040 1080.4750 500.2725 500.2725 500.2725
## 5 500.000 500.000 500.000 500.000 500.000
## 6 500.0225 1036.8142 500.0225 1096.4861 1731.0074
## 7 1106.2970 1189.7152 1236.0323 1140.3828 502.2908
## 393.0495_3.605 427.197_3.64 383.0464_3.601 295.1298_3.641 585.0184_3.643
## 2 17994.1430 3006.5537 1747.9762 19796.1300 1617.0715
## 3 11281.4690 5215.8374 2125.0010 7887.5874 15123.0410
## 4 1547.2397 1750.5648 2248.8909 7556.4023 500.2725
## 5 2493.144 2250.605 1350.122 16807.713 1344.911
## 6 1083.0135 2734.3600 1515.9762 7550.4478 3683.7324
## 7 3176.7600 3191.1912 6360.2870 25827.3540 1398.2491
## 583.0211_3.647 609.0726_3.66 607.0749_3.657 611.069_3.647 250.0718_3.655
## 2 1251.8810 500.0201 1151.8369 1277.2717 20649.0600
## 3 13226.6140 1189.5818 1275.2057 502.2117 5720.1606
## 4 500.2725 500.2725 500.2725 500.2725 10667.2950
## 5 1067.132 500.000 500.000 500.000 11148.359
## 6 4161.7770 500.0225 500.0225 500.0225 10379.8700
## 7 1614.9852 502.2908 1095.3780 1317.0248 9006.9420
## 379.1607_3.652 336.1323_3.647 819.2194_3.7 203.0017_3.661 279.049_3.659
## 2 16945.3570 500.0201 500.0201 45271.3320 1586.7673
## 3 24049.4040 502.2117 502.2117 160459.3300 502.2117
## 4 10625.8910 500.2725 500.2725 54059.4060 1353.7037
## 5 13827.355 500.000 500.000 71851.030 1221.477
## 6 12091.8480 4356.7935 500.0225 38877.6880 11208.2470
## 7 40916.5200 1095.0127 502.2908 44831.3160 1226.9275
## 1303.2955_3.702 413.144_3.692 269.0163_3.668 1003.2656_3.716 222.0406_3.686
## 2 500.0201 1493.2852 500.0201 500.0201 5729.8335
## 3 502.2117 1677.7300 502.2117 502.2117 4179.1010
## 4 500.2725 1590.6039 500.2725 500.2725 4495.7610
## 5 500.000 1199.070 500.000 500.000 6324.002
## 6 500.0225 1636.8853 4438.7334 500.0225 4578.9760
## 7 502.2908 2880.2266 1152.2068 502.2908 6489.9920
## 607.1628_3.711 302.1145_3.69 1069.2473_3.702 841.2146_3.693 194.0458_3.657
## 2 500.0201 91857.8360 500.0201 500.0201 5241.9277
## 3 1142.7068 28181.7800 502.2117 502.2117 8256.8300
## 4 500.2725 15895.0300 500.2725 500.2725 8486.9320
## 5 500.000 25076.066 500.000 500.000 7414.563
## 6 1118.8005 18015.0040 500.0225 500.0225 10004.2940
## 7 1185.6836 8015.8564 502.2908 502.2908 15292.3330
## 835.1973_3.718 334.0517_3.708 638.0934_3.724 374.1587_3.706 295.0806_3.721
## 2 500.0201 500.0201 500.0201 500.0201 2551.5273
## 3 502.2117 502.2117 502.2117 502.2117 1578.5847
## 4 500.2725 500.2725 500.2725 500.2725 6792.6460
## 5 500.000 500.000 500.000 500.000 1362.611
## 6 500.0225 500.0225 500.0225 1364.5600 1525.8048
## 7 502.2908 502.2908 502.2908 502.2908 2028.1727
## 432.1213_3.706 997.2507_3.723 569.1307_3.754 373.1141_3.685 246.0753_3.721
## 2 1086.0332 500.0201 1258.9209 8083.2000 8153.1343
## 3 502.2117 502.2117 1486.8197 8978.2450 3739.3857
## 4 500.2725 500.2725 1545.0271 9669.3850 13370.0540
## 5 500.000 500.000 500.000 4972.398 5864.395
## 6 500.0225 500.0225 1154.3246 41840.9730 7869.1230
## 7 1022.6482 502.2908 2706.1228 18099.8770 6733.2935
## 443.0945_3.705 277.0353_3.723 785.1891_3.723 800.1466_3.728 471.0822_3.708
## 2 1024.4637 1568.9379 500.0201 500.0201 1299.6669
## 3 1124.5013 1134.8145 502.2117 502.2117 1479.6874
## 4 1109.6202 1435.7554 500.2725 500.2725 1441.3151
## 5 1000.000 1656.028 500.000 500.000 1001.342
## 6 1027.0779 2858.1775 500.0225 500.0225 2482.2444
## 7 1072.2911 1298.3939 502.2908 502.2908 1807.2285
## 1159.3041_3.727 1279.2478_3.734 747.2354_3.721 883.1453_3.774 1165.3189_3.729
## 2 500.0201 500.0201 500.0201 500.0201 500.0201
## 3 502.2117 502.2117 502.2117 502.2117 502.2117
## 4 500.2725 500.2725 500.2725 500.2725 500.2725
## 5 500.000 500.000 500.000 500.000 500.000
## 6 500.0225 500.0225 500.0225 500.0225 500.0225
## 7 502.2908 502.2908 502.2908 502.2908 502.2908
## 493.0631_3.69 769.2167_3.733 801.154_3.74 373.1514_3.715 439.1608_3.725
## 2 1971.8566 500.0201 500.0201 1381.0957 5220.8433
## 3 1739.1826 502.2117 502.2117 1860.4106 5158.2593
## 4 2370.0217 500.2725 500.2725 1082.3273 2868.0586
## 5 1546.894 500.000 500.000 1389.819 10824.269
## 6 4162.4580 500.0225 500.0225 2253.5060 6877.0703
## 7 3055.1045 502.2908 1040.0641 2120.3406 6261.7964
## 259.129_3.733 607.1633_3.738 245.0489_3.748 209.0532_3.773 197.0471_3.745
## 2 7273.8794 500.0201 1777.1166 500.0201 500.0201
## 3 4108.9155 502.2117 7832.0625 502.2117 502.2117
## 4 3302.0676 500.2725 2226.3115 1227.0742 500.2725
## 5 2128.313 500.000 5338.036 1355.779 500.000
## 6 2416.1467 1118.8005 6897.7646 7695.1580 5006.8410
## 7 33639.6450 1211.9608 5880.0845 1023.0393 502.2908
## 209.0455_3.749 583.1089_3.742 370.0981_3.747 431.1141_3.739 605.1484_3.739
## 2 500.0201 2004.8652 17987.9800 2087.0034 500.0201
## 3 502.2117 1863.5946 18174.6860 1372.4535 502.2117
## 4 1227.0742 1008.5159 11760.4410 500.2725 500.2725
## 5 500.000 1235.126 5629.892 1137.018 500.000
## 6 7695.1580 2917.7468 10777.8290 1138.9178 500.0225
## 7 1188.3182 2553.2305 28153.0530 1179.0244 1004.5817
## 1003.2655_3.721 447.0928_3.777 889.161_3.783 197.0453_3.76 979.2122_3.736
## 2 500.0201 5817.3184 500.0201 500.0201 500.0201
## 3 502.2117 2607.9136 1028.1815 502.2117 502.2117
## 4 500.2725 1321.8826 500.2725 500.2725 500.2725
## 5 500.000 1646.469 500.000 500.000 500.000
## 6 500.0225 1304.6113 500.0225 5006.8410 500.0225
## 7 502.2908 1368.0901 502.2908 502.2908 502.2908
## 593.1471_3.747 429.082_3.756 417.0951_3.717 109.0288_3.757 336.072_3.762
## 2 500.0201 69880.9400 1424.3392 500.0201 24309.2200
## 3 502.2117 38860.0230 1077.8129 502.2117 18206.6350
## 4 500.2725 500.2725 500.2725 500.2725 17938.7640
## 5 500.000 1068.796 500.000 500.000 10814.509
## 6 500.0225 1000.0450 500.0225 2511.1543 12824.3680
## 7 1070.0051 502.2908 1817.1829 502.2908 16711.3030
## 639.0783_3.758 497.0695_3.785 370.0972_3.765 243.1344_3.787 279.0984_3.799
## 2 6891.4270 13639.4670 10709.7960 79573.8500 1330.3252
## 3 6559.9937 5731.8560 18174.6860 29120.0330 3223.2485
## 4 500.2725 1049.1044 11760.4410 72831.1640 5079.8438
## 5 500.000 500.000 5629.892 13209.661 1466.146
## 6 500.0225 1066.2500 10777.8290 38053.1200 1446.6764
## 7 502.2908 1435.9784 28153.0530 17992.3380 1575.1718
## 1108.0095_3.804 585.0051_3.809 810.9784_3.814 816.9958_3.812 618.1838_3.802
## 2 44856.0600 2189.3810 2182.8752 2483.2370 500.0201
## 3 58556.5100 10752.0950 19287.7400 21774.9060 502.2117
## 4 45225.2850 1067.4426 500.2725 1575.5063 1030.1608
## 5 43306.582 500.000 500.000 500.000 500.000
## 6 57640.1050 1693.0485 1708.6609 2472.7148 500.0225
## 7 47952.0660 5656.9937 7793.5156 12349.9230 1097.2543
## 633.1142_3.808 600.982_3.815 649.0893_3.836 655.1071_3.826 883.1412_3.819
## 2 500.0201 5734.7124 1169.6640 1332.0315 500.0201
## 3 502.2117 37533.9400 502.2117 502.2117 502.2117
## 4 500.2725 3115.7146 1093.6445 1044.4434 500.2725
## 5 500.000 1094.854 500.000 500.000 500.000
## 6 500.0225 4487.0340 1229.8688 500.0225 500.0225
## 7 1303.1267 19456.4300 1350.6036 1026.3260 502.2908
## 606.9991_3.815 458.9733_3.815 412.977_3.815 399.0007_3.816 421.0558_3.818
## 2 12853.4630 12328.3250 15588.7610 22371.0600 1479.3531
## 3 69780.4200 37161.8000 22942.0700 47564.7200 1171.3115
## 4 4775.1460 8345.3830 11561.0220 15273.7170 1430.5155
## 5 2633.967 4435.841 8146.525 10219.755 1229.504
## 6 11777.0960 12020.8020 15848.5460 23609.7830 12992.2400
## 7 39775.2660 21458.2230 22016.2600 39299.3200 1292.7657
## 625.0354_3.814 107.0502_3.816 397.0034_3.815 189.0027_3.815 306.9561_3.814
## 2 1024.6462 36762.2600 198955.5500 103996.8000 34684.1300
## 3 1007.0374 67776.0160 467526.1600 228851.7500 47803.5700
## 4 500.2725 21243.6780 119134.7700 72777.1900 32122.1290
## 5 1035.604 17190.746 68001.510 55028.703 23527.709
## 6 2980.5059 32108.3570 195411.7200 106728.0000 36639.8550
## 7 1322.2114 50042.4300 370974.9000 167055.0200 43562.5700
## 187.0079_3.815 631.0532_3.815 124.0486_3.843 859.0857_3.824 459.093_3.801
## 2 2834762.5000 500.0201 500.0201 500.0201 33601.5200
## 3 5661702.5000 502.2117 502.2117 1100.2384 19828.3460
## 4 1783193.9000 500.2725 500.2725 500.2725 1412.8165
## 5 1285457.800 1042.229 500.000 500.000 1224.051
## 6 2722107.0000 2436.1067 3670.5625 500.0225 1185.4417
## 7 4625648.5000 1033.1272 502.2908 502.2908 1568.9005
## 187.0387_3.806 108.0216_3.832 208.0615_3.836 230.9963_3.838 211.0613_3.866
## 2 95299.8000 500.0201 2304.3200 1002.7122 4237.9336
## 3 120844.6200 502.2117 5911.1685 13124.7980 4617.0283
## 4 68292.0550 500.2725 3792.1785 24363.0300 6673.5120
## 5 50560.727 500.000 8967.401 77042.875 3417.560
## 6 99994.7000 5548.4710 5751.2534 2117.4990 76985.8800
## 7 127672.4700 502.2908 5121.3525 42761.0040 5618.1167
## 336.0719_3.763 212.0937_3.815 211.0971_3.846 187.0101_3.824 123.0452_3.87
## 2 24309.2200 1598.5942 1665.1260 2834762.5000 1613.4084
## 3 18206.6350 1042.9775 502.2117 28989.6640 1807.6850
## 4 17938.7640 1875.5529 1493.0270 37131.5740 2024.4840
## 5 10814.509 1206.836 1288.049 1285457.800 1806.485
## 6 12824.3680 1691.9846 2621.9620 2722107.0000 33680.5660
## 7 16711.3030 1140.2140 2047.3496 4625648.5000 1728.0970
## 403.066_3.865 907.1941_3.871 673.1446_3.853 333.0066_3.893 476.0359_3.919
## 2 500.0201 500.0201 500.0201 17232.4450 1494.5510
## 3 3756.6787 502.2117 1089.0626 47056.8360 1867.0461
## 4 500.2725 500.2725 500.2725 1098.1785 1778.4714
## 5 500.000 500.000 500.000 1530.248 500.000
## 6 1015.0013 500.0225 500.0225 2223.3254 1605.4033
## 7 6201.0117 502.2908 1034.3046 1572.0591 1912.6282
## 446.1142_3.879 679.1625_3.856 913.2118_3.895 461.0857_3.898 335.0215_3.886
## 2 500.0201 500.0201 500.0201 1110.2135 10626.4850
## 3 502.2117 502.2117 502.2117 502.2117 12157.3430
## 4 500.2725 500.2725 500.2725 500.2725 1012.2592
## 5 500.000 500.000 500.000 500.000 1066.522
## 6 1101.0021 500.0225 500.0225 1463.9512 1071.4332
## 7 502.2908 502.2908 502.2908 1278.7991 1256.3544
## 295.0252_3.897 264.0876_3.875 657.1659_3.876 445.1072_3.893 217.0173_3.88
## 2 1162.7778 17601.1450 500.0201 1099.7151 20278.4880
## 3 1033.2161 23898.5780 502.2117 2238.2783 33048.5470
## 4 1106.0292 10057.5160 500.2725 1164.7128 7707.6240
## 5 1192.085 8180.380 500.000 500.000 13451.042
## 6 2119.4683 10463.4760 500.0225 2895.3440 6215.3720
## 7 1432.7365 37543.0900 1183.1326 1339.4325 10430.6820
## 351.07_3.888 431.0979_3.907 751.1822_3.898 283.093_3.891 363.021_3.933
## 2 19412.9400 63325.5740 500.0201 10465.2230 6603.6330
## 3 31765.0620 17252.3400 502.2117 12388.6270 35801.2150
## 4 19264.6070 500.2725 500.2725 7955.6553 1812.1115
## 5 6007.056 500.000 500.000 3184.073 1472.760
## 6 24174.4500 500.0225 500.0225 10926.5240 1213.8076
## 7 39197.2000 1388.0386 502.2908 17390.2000 1470.4209
## 283.0822_3.893 517.1331_3.892 745.1653_3.9 368.0804_3.895 513.0964_3.899
## 2 94270.9300 1225.8290 500.0201 4963.2153 1226.3809
## 3 162427.9200 502.2117 502.2117 21955.5920 1745.1395
## 4 74429.4140 500.2725 500.2725 16235.8670 500.2725
## 5 27075.120 500.000 500.000 4350.932 1058.601
## 6 117184.5900 3785.7686 500.0225 10719.0870 1888.6066
## 7 224133.0200 1204.0728 502.2908 7148.1577 1687.0594
## 217.1081_3.912 372.1116_3.914 434.1396_3.922 432.1447_3.927 331.1218_3.932
## 2 16106.4760 7557.4870 500.0201 500.0201 500.0201
## 3 16787.7700 1222.4287 502.2117 502.2117 4598.1577
## 4 13261.0880 35055.9100 500.2725 500.2725 1406.8314
## 5 9372.490 13287.363 1115.323 500.000 1032.981
## 6 22671.4820 500.0225 500.0225 1660.1782 500.0225
## 7 15859.5300 1118.2866 1079.3500 1137.2874 2514.8323
## 237.0359_3.933 279.0491_3.95 167.0349_3.921 350.088_3.952 584.1374_3.972
## 2 500.0201 1542.5104 1112.2850 65342.4700 1059.7166
## 3 1216.0801 1401.9796 502.2117 94949.5000 502.2117
## 4 1077.5962 1434.3649 1655.5316 79700.3100 500.2725
## 5 1132.075 1422.325 1029.521 11276.089 500.000
## 6 500.0225 26237.7710 1593.4023 24731.6330 1866.1840
## 7 1188.8030 1348.4651 1957.2369 28154.8320 1459.4160
## 210.0419_3.959 396.1279_4.011 1122.0249_4.032 337.1446_4.025 614.1849_4.02
## 2 500.0201 500.0201 19439.8140 1616.4110 500.0201
## 3 502.2117 1576.9596 27973.2340 1111.9446 502.2117
## 4 500.2725 500.2725 1021.6447 1552.3207 500.2725
## 5 500.000 500.000 500.000 500.000 500.000
## 6 500.0225 500.0225 500.0225 1285.7370 500.0225
## 7 502.2908 1235.6437 502.2908 1302.7362 502.2908
## 427.197_4.049 194.0491_4.031 662.1454_4.018 514.136_3.993 428.0955_4.037
## 2 3337.3948 14809.8820 500.0201 1812.8403 500.0201
## 3 6070.5190 20284.2340 502.2117 3762.1628 1061.6772
## 4 2754.9048 17819.6390 500.2725 2912.2285 500.2725
## 5 3383.081 20059.430 500.000 1272.891 500.000
## 6 3190.0864 27963.5430 500.0225 1481.6903 1196.3405
## 7 4479.4890 47632.6250 502.2908 1670.4417 502.2908
## 150.056_4.032 656.1287_4.025 216.0277_4.034 194.0459_4.033 313.995_4.031
## 2 1134.0117 500.0201 1784.0631 14809.8820 1360.2754
## 3 1613.3573 502.2117 1692.0667 20284.2340 2456.6650
## 4 1438.1763 500.2725 2174.2688 17819.6390 2465.7056
## 5 1544.032 500.000 2707.154 20059.430 1854.125
## 6 2385.1660 500.0225 2134.5017 27963.5430 2813.4988
## 7 3688.2927 502.2908 4192.8840 47632.6250 4045.3906
## 678.1113_4.032 411.0759_4.059 281.1142_4.039 429.0815_4.032 606.1191_4.053
## 2 500.0201 3425.7554 12571.5180 42738.9300 1031.2367
## 3 502.2117 3658.3208 13000.7930 33700.7800 1118.6216
## 4 500.2725 3619.0388 14959.9410 500.2725 1032.2228
## 5 500.000 2601.438 7128.849 500.000 1012.195
## 6 500.0225 2628.2961 11598.5800 1191.8750 500.0225
## 7 502.2908 1806.5068 11206.6930 1244.0428 502.2908
## 433.0646_4.045 373.1139_4.04 495.223_4.067 255.1349_4.042 379.1607_4.047
## 2 6830.7570 15089.2660 1794.7240 43546.0470 30632.3950
## 3 5472.2750 14828.4090 1200.6797 115880.2100 43233.9020
## 4 1083.8410 20148.7340 1311.6964 92772.0700 17412.6950
## 5 500.000 7662.456 1129.322 13672.811 20755.982
## 6 1128.6172 15213.1190 1113.5663 43302.2770 21854.0600
## 7 1092.2622 28159.1000 13910.3040 21668.3800 80555.9100
## 401.1434_4.052 184.0979_4.054 571.1476_4.081 201.0227_4.086 417.1173_4.046
## 2 8576.2990 8380.1650 500.0201 276003.0600 1107.0532
## 3 10649.2420 15776.3730 502.2117 566404.6000 1265.0997
## 4 4732.8823 5861.9404 1209.0028 2771.8438 500.2725
## 5 5780.745 22387.355 500.000 2410.425 500.000
## 6 6853.7764 10352.3700 500.0225 2831.0718 1369.1118
## 7 18687.4700 10717.2240 1363.8920 2442.7874 1511.8750
## 351.1236_4.04 562.1922_4.035 397.1911_4.067 367.2236_4.071 447.0921_4.067
## 2 1080.4198 500.0201 500.0201 18045.5400 8853.1700
## 3 502.2117 1210.4100 502.2117 25799.0040 9127.3290
## 4 1200.1506 1155.6400 500.2725 18495.1210 1358.0940
## 5 500.000 500.000 500.000 10054.541 16708.617
## 6 1494.5300 500.0225 500.0225 21946.8850 5332.6406
## 7 1529.7859 1544.2903 502.2908 30512.0490 6793.3160
## 375.1294_4.074 181.0507_4.062 297.0975_4.072 489.2123_4.076 201.0227_4.09
## 2 9512.9060 500.0201 118699.6950 500.0201 276003.0600
## 3 8880.4990 502.2117 97780.7500 12474.1380 566404.6000
## 4 12815.9700 1146.7318 1337.3105 1802.8375 2771.8438
## 5 10566.450 1582.588 1780.940 500.000 2410.425
## 6 11239.8100 9212.3840 1384.6410 500.0225 2831.0718
## 7 19962.2800 2154.8665 2276.9860 1123.0166 2442.7874
## 765.197_4.127 365.0863_4.095 680.2306_4.136 151.04_4.082 853.2516_4.121
## 2 1007.2653 23400.2130 500.0201 2087.7424 500.0201
## 3 502.2117 20392.6020 502.2117 5241.4450 502.2117
## 4 500.2725 1099.2460 500.2725 5335.6807 500.2725
## 5 500.000 1189.499 500.000 2476.194 500.000
## 6 500.0225 1167.1471 500.0225 2929.0605 500.0225
## 7 1107.9886 1638.7913 502.2908 2539.6985 1379.6024
## 165.0557_4.102 204.0666_4.129 681.2357_4.115 247.0798_4.12 908.2639_4.165
## 2 15463.0880 16845.3070 1350.4375 1013.4516 500.0201
## 3 14762.5730 5050.5063 502.2117 1824.5050 502.2117
## 4 6505.9365 9398.2470 500.2725 2595.5125 500.2725
## 5 5094.479 6261.242 500.000 5533.848 500.000
## 6 6902.4536 7298.9946 500.0225 1768.2439 500.0225
## 7 81986.0800 8835.7730 502.2908 5627.4920 502.2908
## 185.0819_4.129 246.0752_4.139 531.1447_4.141 497.2379_4.163 1122.0248_4.125
## 2 31914.3950 9816.3860 1195.2208 1588.1838 18176.4900
## 3 31678.7660 5029.8696 1455.2782 1170.0887 31517.2850
## 4 39332.9060 15328.4990 1152.6781 1633.1454 500.2725
## 5 20293.197 6565.389 1076.325 1250.345 500.000
## 6 43057.2340 9835.3740 1312.1841 1418.8896 500.0225
## 7 54471.0550 8589.8850 1625.8005 3376.2478 502.2908
## 845.2477_4.186 363.1658_4.172 418.1327_4.164 565.0619_4.188 569.1299_4.178
## 2 500.0201 7593.4790 500.0201 1276.8286 1245.6401
## 3 502.2117 9047.3120 502.2117 2187.6565 1562.2822
## 4 500.2725 12075.4390 500.2725 500.2725 500.2725
## 5 500.000 11406.414 500.000 500.000 1309.736
## 6 1145.1302 38251.9960 500.0225 3525.0964 1004.5967
## 7 1203.7396 78509.0900 502.2908 1854.6865 1456.9252
## 245.0488_4.187 675.1511_4.19 691.3179_4.171 813.255_4.198 345.1933_4.201
## 2 3896.8047 500.0201 1084.5996 500.0201 1314.3209
## 3 4913.7515 1018.6769 1226.0865 502.2117 1571.9619
## 4 3427.6433 500.2725 1213.0780 500.2725 1963.8844
## 5 13658.554 500.000 1055.245 500.000 1145.560
## 6 26215.6680 500.0225 1061.5448 500.0225 2622.9211
## 7 20793.8630 502.2908 13143.5880 1297.0577 19960.6880
## 1041.2898_4.199 579.2066_4.211 714.3024_4.193 710.0906_4.208 461.0817_4.193
## 2 500.0201 1096.4995 500.0201 500.0201 1559.1384
## 3 502.2117 502.2117 502.2117 502.2117 502.2117
## 4 500.2725 1189.3319 500.2725 500.2725 1018.1447
## 5 500.000 500.000 500.000 500.000 1307.606
## 6 500.0225 4442.6600 500.0225 500.0225 1383.2630
## 7 502.2908 1456.6451 8200.0220 502.2908 1259.8547
## 285.0968_4.206 211.0967_4.185 431.1146_4.185 913.2113_4.211 485.0499_4.198
## 2 1591.0035 2141.0100 1492.3569 500.0201 500.0201
## 3 1198.2834 1366.0497 2108.5652 502.2117 1066.2404
## 4 1347.8960 1686.0962 1187.8210 500.2725 500.2725
## 5 1243.912 1193.337 500.000 500.000 500.000
## 6 2118.4414 6931.2485 1646.1582 500.0225 1221.0613
## 7 1457.1495 1860.4698 2051.8480 502.2908 1110.7401
## 807.2393_4.206 413.1426_4.219 137.0244_4.225 891.2172_4.205 1141.2441_4.201
## 2 500.0201 2777.5090 1388.5740 500.0201 500.0201
## 3 502.2117 14651.1190 1873.6206 502.2117 502.2117
## 4 500.2725 7951.3140 1760.3980 500.2725 500.2725
## 5 500.000 4579.056 2756.803 500.000 500.000
## 6 500.0225 13593.5660 3097.1704 500.0225 500.0225
## 7 1009.4717 72335.5860 4428.3840 502.2908 502.2908
## 673.1455_4.198 476.0415_4.203 465.0845_4.208 123.0452_4.211 445.1115_4.233
## 2 500.0201 500.0201 1415.1337 2133.5618 1439.3954
## 3 502.2117 1049.3372 2255.6487 1047.7245 2993.4424
## 4 500.2725 500.2725 1904.9906 2364.5676 1467.6023
## 5 500.000 500.000 1205.233 1380.799 1369.613
## 6 500.0225 500.0225 2061.0295 97200.3360 14130.6420
## 7 502.2908 502.2908 6454.0320 1342.1523 2321.4463
## 212.0968_4.21 108.0218_4.217 907.1956_4.201 679.1604_4.197 892.2207_4.211
## 2 500.0201 500.0201 500.0201 1211.0000 500.0201
## 3 502.2117 502.2117 502.2117 1610.2859 502.2117
## 4 500.2725 500.2725 500.2725 1168.6095 1006.4688
## 5 500.000 500.000 500.000 500.000 500.000
## 6 1060.3683 15684.2350 500.0225 1703.8150 500.0225
## 7 502.2908 502.2908 502.2908 1724.7579 502.2908
## 689.1166_4.213 488.0429_4.206 657.1681_4.216 815.077_4.202 1135.2278_4.215
## 2 500.0201 500.0201 1088.0382 500.0201 500.0201
## 3 1767.0101 502.2117 502.2117 502.2117 502.2117
## 4 500.2725 500.2725 500.2725 500.2725 500.2725
## 5 500.000 500.000 500.000 500.000 500.000
## 6 500.0225 500.0225 500.0225 500.0225 500.0225
## 7 502.2908 502.2908 502.2908 502.2908 502.2908
## 447.092_4.187 559.0453_4.234 347.1678_4.233 361.1501_4.225 1132.0633_4.207
## 2 14209.7500 500.0201 1978.9418 18063.7150 500.0201
## 3 6538.0137 502.2117 4885.0073 48543.9380 502.2117
## 4 1221.7495 500.2725 6896.6960 13687.7770 500.2725
## 5 9892.394 1210.506 4696.693 12739.073 500.000
## 6 3765.2014 2102.8110 5900.6206 17000.6580 2203.8574
## 7 4402.0910 1061.7600 20057.3160 31987.8360 502.2908
## 439.1607_4.307 466.0888_4.204 401.1094_4.24 503.2011_4.23 211.0615_4.235
## 2 35630.7420 1155.8306 1356.3113 500.0201 9299.7800
## 3 23377.7200 1212.0710 1017.2500 502.2117 5661.7524
## 4 32249.9840 1130.3230 1140.9067 1004.0062 14144.8170
## 5 23424.883 500.000 1192.040 500.000 6879.601
## 6 20375.0550 1008.7882 12783.1430 2787.1323 222310.8100
## 7 48064.6640 2062.6162 1082.3772 1340.5103 11281.4460
## 295.0815_4.242 363.0692_4.266 269.1506_4.275 481.0163_4.286 361.0666_4.291
## 2 5294.9746 1719.5027 55917.7420 500.0201 500.0201
## 3 1994.1454 1519.1920 56339.6800 1070.1798 502.2117
## 4 12367.1240 3087.0342 106905.8600 1194.2478 1052.2242
## 5 1849.375 1509.568 61634.270 1139.437 1005.257
## 6 1354.1439 1696.7412 65104.3100 500.0225 1268.7782
## 7 2633.7764 2583.0503 97614.0860 1564.8662 1169.2520
## 459.0361_4.296 745.1266_4.269 429.0571_4.306 265.1192_4.316 449.1074_4.301
## 2 1225.6070 500.0201 500.0201 13066.9110 5779.1270
## 3 1258.0916 502.2117 502.2117 15573.8610 4929.6520
## 4 1099.7789 500.2725 500.2725 22505.9510 1634.0016
## 5 1185.275 500.000 1181.836 18379.248 1670.182
## 6 1223.4037 500.0225 500.0225 14431.5550 500.0225
## 7 1103.3473 502.2908 1193.2788 30736.5470 1889.6991
## 199.0068_4.328 333.0047_4.33 459.1504_4.33 492.1884_4.319 252.0891_4.335
## 2 22453.9340 40354.1760 27175.8360 500.0201 500.0201
## 3 32792.8870 159986.2800 16161.5570 502.2117 502.2117
## 4 123501.5900 1782.3069 9640.4700 500.2725 500.2725
## 5 44842.332 1665.914 11853.272 500.000 500.000
## 6 7873.2603 2017.9968 13427.3520 500.0225 500.0225
## 7 9935.0310 2137.6326 11292.5650 502.2908 502.2908
## 572.2528_4.339 197.0819_4.344 363.1657_4.345 359.1344_4.351 229.017_4.354
## 2 17158.7680 27600.6270 13217.0990 2929.5144 3282.8772
## 3 5759.8003 18742.1880 6192.3810 10011.0530 25208.5880
## 4 4977.1777 37192.6900 10307.2170 4351.0894 10712.5590
## 5 4262.561 16994.816 3932.929 1805.749 12468.570
## 6 12581.1950 42092.0860 12115.0640 3142.5696 16434.3140
## 7 13409.0150 47107.3870 9359.7760 20665.8570 16348.3190
## 385.1685_4.355 295.0242_4.354 335.0244_4.36 640.3332_4.371 243.0329_4.371
## 2 27485.6500 1037.8563 30748.2830 5514.1333 2071.1892
## 3 5134.5864 1361.5409 44077.8100 6525.2646 9016.1200
## 4 22228.1740 1694.0413 1166.7769 7909.3857 4185.2940
## 5 12024.319 1146.532 1948.247 6326.681 6490.809
## 6 41181.6900 1123.1445 1594.8630 2940.4482 5720.7095
## 7 20923.7970 1223.4404 1316.0590 12598.6220 7917.5600
## 446.1831_4.341 425.145_4.374 514.1681_4.367 343.0855_4.396 495.1174_4.369
## 2 1560.2617 14722.6870 500.0201 8483.2580 3014.2500
## 3 1121.1812 4872.1855 502.2117 28780.3480 1872.8999
## 4 1361.3481 9586.8955 1026.9316 8179.7170 1532.4000
## 5 500.000 11847.822 500.000 24581.084 1267.245
## 6 8808.3600 8369.7480 2427.3208 5976.2163 1544.0000
## 7 1294.7365 13045.5400 1197.7728 12347.5630 3046.0417
## 217.0172_4.4 397.1895_4.429 533.1257_4.426 527.1086_4.425 455.1062_4.407
## 2 58557.6330 500.0201 1439.5072 1117.9026 1394.1925
## 3 47241.5300 502.2117 1081.0302 1266.9210 3071.6780
## 4 4373.0186 1564.3976 500.2725 1091.9269 500.2725
## 5 8832.745 500.000 1340.569 500.000 500.000
## 6 3322.8098 1122.5442 1077.5782 1105.8402 500.0225
## 7 12286.2250 1581.0319 1646.5192 1971.3319 1037.0580
## 363.0234_4.453 517.0977_4.399 465.1366_4.417 397.1496_4.415 817.2886_4.438
## 2 14996.7440 1303.6475 1261.2980 1728.5679 500.0201
## 3 88644.1500 502.2117 1157.9459 1439.8370 502.2117
## 4 9155.0590 1515.7781 500.2725 1454.4977 500.2725
## 5 4044.874 500.000 500.000 1406.541 500.000
## 6 3701.5623 1636.8439 1123.7861 2502.4536 500.0225
## 7 4041.2890 1484.1147 2097.8298 3281.9387 1516.2362
## 485.1661_4.43 361.0646_4.442 363.0701_4.451 429.0565_4.442 459.0924_4.452
## 2 28476.8400 500.0201 1267.2815 1903.0104 1083.5731
## 3 25053.0980 502.2117 2514.6628 502.2117 1466.3830
## 4 33872.0000 500.2725 1640.8085 1088.0776 500.2725
## 5 18329.996 1200.162 1702.103 500.000 1342.490
## 6 21684.7440 1011.9418 1719.3994 500.0225 1197.8843
## 7 16546.4820 1386.5675 1816.7646 502.2908 1592.6484
## 391.0993_4.492 445.0782_4.484 396.1124_4.459 427.1588_4.465 405.1764_4.467
## 2 2134.0005 36958.0860 3297.0050 18692.2660 75606.0550
## 3 1858.5024 9651.5580 8763.8820 27626.5410 105838.6100
## 4 2473.1365 1310.5109 3563.5900 10963.8770 36379.2970
## 5 1274.227 1040.282 2283.335 13559.532 50422.453
## 6 2064.2227 1429.5366 8070.8980 13868.0790 46699.6900
## 7 1378.0276 3928.9548 10723.8710 28537.1640 130460.7600
## 565.1557_4.485 243.0779_4.486 245.0937_4.487 417.1192_4.5 509.0959_4.5
## 2 1897.1666 98969.2600 34343.2070 500.0201 1274.5331
## 3 1021.9170 52586.6760 8035.0215 502.2117 1197.6566
## 4 1644.3389 45230.1400 6770.9424 500.2725 1364.6416
## 5 1152.575 43097.770 10609.345 500.000 1022.691
## 6 7180.3640 21415.4220 5492.7200 1072.8165 1218.3741
## 7 1630.7605 39697.0300 7075.2400 1442.2354 1625.5409
## 212.0065_4.503 580.1557_4.511 297.1062_4.504 365.0866_4.514 297.0975_4.498
## 2 500.0201 500.0201 18571.6330 46667.7900 265259.0600
## 3 1022.4869 502.2117 17202.4800 35284.9500 211687.7000
## 4 500.2725 500.2725 500.2725 1740.0006 1403.2051
## 5 500.000 500.000 500.000 1288.222 1367.149
## 6 500.0225 500.0225 1220.2224 1295.2630 1423.8284
## 7 1486.0110 502.2908 1410.9357 2355.2570 2220.7412
## 617.1836_4.515 501.1339_4.505 447.0919_4.504 181.0503_4.495 795.2377_4.518
## 2 6491.5957 1356.5575 12255.3770 1029.6250 500.0201
## 3 4387.0020 1064.6024 12365.8220 502.2117 1082.4540
## 4 500.2725 500.2725 1192.0284 500.2725 500.2725
## 5 1129.443 1015.445 26084.623 1111.155 500.000
## 6 1008.0633 1118.3269 6507.1313 15393.7630 500.0225
## 7 1075.1255 2011.5920 8644.8710 1972.9110 502.2908
## 801.2549_4.519 549.1948_4.514 443.1248_4.497 345.1938_4.506 521.1082_4.508
## 2 500.0201 1080.6757 1314.9849 1267.3481 10740.6610
## 3 4069.1458 1090.1663 4971.6084 1438.2470 13150.9375
## 4 500.2725 1070.9548 1884.1931 2342.1475 1017.5289
## 5 500.000 500.000 1631.061 1537.169 1401.632
## 6 500.0225 1173.4874 3531.4421 2251.9114 1400.6052
## 7 502.2908 502.2908 17172.1270 32250.7950 1293.2250
## 411.1279_4.49 411.1654_4.559 714.3028_4.511 692.32_4.516 833.2468_4.513
## 2 3198.3208 2218.7420 500.0201 500.0201 500.0201
## 3 1807.5377 1645.8203 502.2117 502.2117 1170.9766
## 4 1960.1522 2130.8347 500.2725 500.2725 500.2725
## 5 1357.588 3635.758 500.000 500.000 500.000
## 6 2660.6000 2767.2250 500.0225 500.0225 500.0225
## 7 2799.5560 5365.7260 16415.5300 13039.1040 8833.0330
## 481.1298_4.503 713.2993_4.51 465.1045_4.511 475.1137_4.509 403.1133_4.514
## 2 1835.2778 500.0201 1859.0535 1847.5371 1518.1235
## 3 7389.5327 2150.7368 11710.7960 7959.3610 6069.4570
## 4 3776.6536 1023.2011 5262.3280 3925.9343 3340.6191
## 5 3245.185 500.000 2839.534 1782.431 2240.783
## 6 6306.4340 1622.5605 7581.4556 4740.9100 6226.2670
## 7 28111.5350 44106.5740 51713.2900 23622.3700 32393.1540
## 691.3175_4.507 345.1554_4.506 779.2721_4.515 433.1136_4.519 792.3445_4.502
## 2 500.0201 20852.3030 1000.0402 36876.1050 500.0201
## 3 1424.4734 147361.2700 502.2117 130782.1000 502.2117
## 4 500.2725 63373.2660 500.2725 1524.9140 500.2725
## 5 500.000 28485.637 500.000 1178.540 500.000
## 6 1198.5745 105147.3800 1074.5065 2467.6080 500.0225
## 7 34889.7770 779903.9000 502.2908 5195.4673 502.2908
## 413.1426_4.51 544.1472_4.514 345.1556_4.509 446.1795_4.499 814.3239_4.509
## 2 3822.1885 1002.5149 20852.3030 1210.6852 500.0201
## 3 22333.0230 502.2117 147361.2700 1340.8474 502.2117
## 4 12796.6170 500.2725 63373.2660 1146.6337 500.2725
## 5 5930.739 500.000 28485.637 500.000 500.000
## 6 19394.1520 1094.1141 105147.3800 9838.2500 500.0225
## 7 108875.2300 502.2908 41562.0980 1084.7963 502.2908
## 572.2105_4.512 491.191_4.507 514.1698_4.543 341.1234_4.519 531.0773_4.532
## 2 500.0201 500.0201 1124.3052 3259.0725 4715.7686
## 3 502.2117 502.2117 502.2117 1429.3102 12150.3150
## 4 500.2725 500.2725 500.2725 1860.2935 1904.4490
## 5 500.000 500.000 500.000 2248.730 1446.962
## 6 500.0225 500.0225 2525.5530 12178.7810 1547.4824
## 7 2657.9800 1205.8857 502.2908 22193.3550 1471.4215
## 389.0919_4.531 541.2651_4.526 385.1861_4.524 204.0666_4.532 272.0538_4.533
## 2 30853.8380 49211.2200 8278.1350 56397.7420 11972.3990
## 3 24835.9410 33827.0080 9187.4600 5907.1055 1354.5878
## 4 30947.1270 41123.1520 3388.3657 31545.1680 7493.1416
## 5 21487.709 26071.283 13857.561 20137.030 4345.635
## 6 24255.9570 45082.9770 8629.1430 22654.2070 4862.6950
## 7 11179.1610 19061.7000 9608.0625 21858.1700 5151.4517
## 224.062_4.537 383.1529_4.559 425.0363_4.553 201.0228_4.555 203.0172_4.555
## 2 21967.3700 27211.6950 40519.2930 1109838.1000 49373.6520
## 3 5963.2144 6209.7495 163893.1900 2740269.2000 107369.7000
## 4 17189.0300 17199.4980 1391.3663 8847.9020 500.2725
## 5 9347.290 9585.461 500.000 4505.520 1247.953
## 6 20445.3960 34320.6200 1161.2998 8279.4920 1348.0198
## 7 11412.6850 10278.6220 1670.5409 4023.0327 1271.0688
## 1122.0244_4.55 493.1666_4.573 366.0851_4.55 269.1614_4.571 270.1538_4.572
## 2 38332.2420 8883.6770 11007.1720 12831.2860 24905.3750
## 3 48068.5940 12363.5110 8576.7040 11171.7190 20781.9600
## 4 1629.8573 1725.0573 500.2725 23895.3380 48914.4800
## 5 1559.223 1870.743 1732.607 13119.943 26293.598
## 6 1102.7025 2252.5078 500.0225 10984.5340 29178.0500
## 7 502.2908 2590.3523 4142.9850 18660.1070 37244.0430
## 269.1505_4.575 389.0981_4.564 561.29_4.571 151.04_4.576 536.1898_4.583
## 2 168327.2300 11812.0460 1980.8004 1379.4852 500.0201
## 3 131175.2000 8569.8790 1417.0460 7649.9014 502.2117
## 4 304923.7000 22971.7050 8845.7830 12576.1455 500.2725
## 5 166453.530 10959.376 2650.157 2479.001 500.000
## 6 170655.0500 11111.5205 2065.5764 3811.4640 1092.3750
## 7 211573.1400 12799.5980 3872.6875 3606.0684 502.2908
## 355.1037_4.621 238.0776_4.607 507.2078_4.607 237.1243_4.614 550.2061_4.628
## 2 1749.6000 11613.2550 1238.8121 6867.7940 1039.6333
## 3 3452.0479 3164.5040 4206.1360 18515.7620 502.2117
## 4 2007.5636 17256.9320 5436.7163 19349.8650 500.2725
## 5 1279.937 8977.313 4871.195 25809.947 500.000
## 6 5165.5127 14343.5070 11767.0720 10065.1700 500.0225
## 7 13623.0870 7438.6640 5104.5513 45283.1250 502.2908
## 482.2186_4.626 602.1667_4.638 618.1913_4.649 400.1429_4.669 440.1641_4.66
## 2 500.0201 500.0201 1001.4805 4314.1313 22935.5310
## 3 502.2117 502.2117 502.2117 5263.6060 14449.4760
## 4 500.2725 500.2725 500.2725 3461.4475 18276.0270
## 5 500.000 500.000 500.000 2466.310 12178.715
## 6 500.0225 500.0225 500.0225 1867.3164 12900.1890
## 7 1340.7145 502.2908 502.2908 4643.1390 31328.1370
## 461.1426_4.66 439.1608_4.66 539.2494_4.677 477.1039_4.686 245.0485_4.677
## 2 22342.8300 84061.9600 52808.7300 1336.4696 13326.7720
## 3 13347.7220 47145.1400 41944.2030 1394.1194 1610.6150
## 4 18871.0570 70785.0800 45355.2900 500.2725 6064.7495
## 5 14962.760 50771.340 27440.580 500.000 40403.617
## 6 13647.0330 49392.3320 38826.9060 1266.9812 59262.8500
## 7 29214.3520 127771.7400 16958.5040 1292.3481 52591.4300
## 395.1144_4.677 354.1007_4.717 473.1448_4.73 600.2843_4.732 339.1082_4.74
## 2 500.0201 3306.9010 27960.9120 81664.0100 4630.5767
## 3 502.2117 12077.5050 2878.7468 25238.9080 3142.1590
## 4 1031.8560 3519.6260 10558.7110 28730.5370 5125.0107
## 5 500.000 2596.864 4601.004 32069.043 5948.137
## 6 500.0225 2501.9280 1864.7656 50578.0400 5521.8984
## 7 1044.7402 14006.8850 17641.1620 34181.2100 16994.5570
## 305.0561_4.737 690.3154_4.751 243.1236_4.74 175.0975_4.743 343.0855_4.765
## 2 500.0201 1196.3077 16059.1480 500.0201 18579.6050
## 3 1489.6871 6130.7104 12399.7705 1557.9905 75148.2660
## 4 1089.7163 5252.0000 18358.4600 1878.2666 20522.6330
## 5 1152.188 500.000 11756.490 500.000 56479.830
## 6 500.0225 500.0225 20118.5620 1349.3058 16951.3180
## 7 1038.4114 5154.5100 56901.9570 1206.2797 27130.4700
## 220.0979_4.733 415.0625_4.788 347.0774_4.782 171.0487_4.824 541.2651_4.807
## 2 1638.4552 1021.1602 500.0201 500.0201 122914.7800
## 3 1724.1215 502.2117 1041.0525 502.2117 75771.5600
## 4 1005.2814 500.2725 500.2725 500.2725 88029.5000
## 5 500.000 500.000 500.000 500.000 53759.320
## 6 500.0225 1044.3330 500.0225 500.0225 95655.7100
## 7 1865.9061 502.2908 1173.3026 502.2908 38066.9600
## 137.0243_4.815 495.2227_4.812 517.2049_4.804 565.1563_4.799 366.1016_4.846
## 2 1662.0676 11146.9280 2882.8188 2647.6060 3199.0903
## 3 3193.0107 1366.4086 502.2117 1756.9518 23928.9410
## 4 2641.7173 2352.6565 500.2725 1647.2811 5646.1357
## 5 5506.447 1095.126 500.000 1374.983 7875.066
## 6 4906.6880 1566.5435 500.0225 17029.7640 5112.7495
## 7 8481.0830 66939.6700 17211.6200 1484.8522 5723.1520
## 411.2021_4.884 383.153_4.885 507.2079_4.889 433.113_4.886 553.0631_4.891
## 2 11232.7540 70643.2900 1886.9634 102240.2200 9312.3955
## 3 17214.3930 10736.6020 7853.3810 329724.4400 32912.5430
## 4 14588.9550 29128.1230 11057.5030 500.2725 500.2725
## 5 15264.835 15596.135 5999.206 500.000 500.000
## 6 18982.3100 71164.5800 17203.4160 1123.0288 500.0225
## 7 34991.2230 16922.4320 6678.3164 1115.3750 1093.7258
## 427.1967_4.894 448.1966_4.911 541.265_4.928 493.228_4.95 357.1009_4.96
## 2 3329.2760 500.0201 106122.6900 1161.5969 12913.0570
## 3 3412.5671 502.2117 86854.4900 2668.9990 9450.5720
## 4 2824.1270 500.2725 123232.4400 4901.6230 10193.0880
## 5 3092.959 500.000 50948.100 2959.201 5238.429
## 6 3632.7210 500.0225 84515.4200 4996.1560 4483.3457
## 7 10620.6570 1370.2866 37987.1170 2811.0500 11063.6400
## 433.2077_4.995 462.1766_4.998 407.0405_4.991 591.3184_5.021 294.149_5.025
## 2 50237.2770 20843.9650 1233.0165 4959.5977 500.0201
## 3 65668.1700 40955.9920 1171.7200 1760.4808 502.2117
## 4 23353.0270 42852.5200 1328.6544 500.2725 500.2725
## 5 39260.465 14111.677 500.000 5026.287 500.000
## 6 34034.7730 30898.1820 1194.6644 1887.0361 500.0225
## 7 136370.0200 26898.5410 1237.9250 10924.7770 502.2908
## 442.1246_5.012 199.0975_5.017 198.1135_5.021 436.1064_5.004 403.1019_5.042
## 2 500.0201 31569.9840 20392.4020 500.0201 17884.0210
## 3 502.2117 43998.1680 24181.2440 502.2117 1201.3427
## 4 500.2725 28701.6370 22313.4450 500.2725 1341.4708
## 5 500.000 23920.473 31832.006 500.000 1294.283
## 6 500.0225 45910.3700 35357.3240 500.0225 60392.4650
## 7 1045.8989 75289.9300 46829.7230 502.2908 7142.3745
## 374.1378_5.008 236.1018_5.033 354.1009_5.03 589.3024_5.037 253.0503_5.048
## 2 500.0201 500.0201 12059.9780 1183.0342 87628.7900
## 3 502.2117 502.2117 38183.9770 502.2117 9273.5380
## 4 500.2725 500.2725 9882.3050 500.2725 500.2725
## 5 500.000 1208.283 8366.475 11970.824 500.000
## 6 500.0225 1103.7936 7548.2890 5399.0730 500.0225
## 7 502.2908 1434.4534 49713.6950 12103.9480 502.2908
## 593.3333_5.048 539.1147_5.072 377.1019_5.09 255.0674_5.078 509.2751_5.084
## 2 20812.2970 500.0201 1027.2092 25627.5120 12026.2440
## 3 502.2117 1068.5839 1151.8346 2107.4490 8152.4160
## 4 500.2725 1030.7670 500.2725 500.2725 16909.2730
## 5 500.000 1290.289 500.000 500.000 11089.767
## 6 1264.2401 3301.1820 1162.5295 1099.6250 8110.6300
## 7 2037.0231 1504.2439 2033.6578 1006.4058 10047.3220
## 525.27_5.101 387.1658_5.117 544.2886_5.154 366.1015_5.18 221.1178_5.185
## 2 19517.9260 22937.2130 1040.8656 8198.0720 500.0201
## 3 14197.8360 20481.3480 1061.0623 72130.9840 502.2117
## 4 16485.2130 41533.5200 500.2725 19640.7030 500.2725
## 5 5771.371 22260.062 500.000 25038.250 500.000
## 6 12939.4610 23806.8960 1118.5981 19572.6780 1199.3672
## 7 7958.5080 53824.0230 502.2908 19473.2970 502.2908
## 177.1283_5.202 213.1132_5.201 511.2544_5.206 502.1148_5.219 352.0859_5.224
## 2 500.0201 24307.0880 5852.8706 500.0201 112688.7200
## 3 502.2117 33047.1500 3714.0657 502.2117 458347.8800
## 4 500.2725 34331.5860 11037.7140 500.2725 262000.7000
## 5 500.000 23485.188 4631.938 500.000 100043.336
## 6 500.0225 60858.3550 4175.9900 500.0225 208584.6600
## 7 502.2908 31735.4060 5967.0386 502.2908 182137.0500
## 218.0821_5.22 317.1602_5.227 385.1498_5.181 235.0957_5.223 492.1871_5.243
## 2 1588.3785 2098.5276 3198.5620 5796.4800 500.0201
## 3 17984.6000 1498.5624 2005.3846 6726.1680 502.2117
## 4 4179.0130 1280.7831 3541.4400 7509.8843 1018.1037
## 5 2000.520 1878.778 2268.223 5944.811 500.000
## 6 4259.6465 53313.6130 12095.8500 13241.8540 500.0225
## 7 21668.5370 3814.2888 4596.5900 6477.6914 502.2908
## 283.1657_5.239 481.2439_5.242 549.2312_5.243 493.1858_5.275 601.2255_5.243
## 2 15629.7070 96519.6900 17937.1540 1343.4958 1291.3202
## 3 14522.3880 46078.2800 11311.9210 1473.6250 1708.3146
## 4 18881.8340 92854.0200 17842.0530 500.2725 1841.1512
## 5 4988.767 46593.100 11252.220 1008.909 500.000
## 6 16454.7700 47444.9260 7793.1904 1020.0914 1166.7775
## 7 5726.3470 23718.9700 5081.6660 1117.0500 1741.1820
## 651.3856_5.28 537.1508_5.278 191.1105_5.283 223.0969_5.259 509.1453_5.277
## 2 2426.2744 1347.7834 2221.5210 3237.1140 1021.5819
## 3 22397.5740 1166.1658 502.2117 3351.8425 1093.4860
## 4 2509.8520 500.2725 500.2725 3574.7430 1220.8693
## 5 11791.816 1281.835 500.000 3418.265 500.000
## 6 5622.4517 1298.5032 2148.1370 5141.1630 500.0225
## 7 2543.0955 1387.5708 1963.0996 5629.7944 1069.8583
## 257.0801_5.284 73.0294_5.277 471.199_5.262 515.1648_5.296 355.0445_5.237
## 2 1097.2024 500.0201 500.0201 1029.4464 1697.0703
## 3 502.2117 1118.5565 502.2117 1324.2821 2313.2449
## 4 500.2725 500.2725 500.2725 500.2725 2196.7756
## 5 500.000 500.000 500.000 500.000 1403.233
## 6 1577.2256 1048.8716 1329.5245 500.0225 4114.3020
## 7 1487.7784 502.2908 1528.0974 1205.0000 1984.3488
## 191.1076_5.282 433.2076_5.283 223.0982_5.267 531.1311_5.287 536.3209_5.3
## 2 2221.5210 19980.6910 2078.3364 500.0201 500.0201
## 3 502.2117 22105.5230 3351.8425 502.2117 502.2117
## 4 500.2725 15200.3810 1130.0481 1148.1954 500.2725
## 5 500.000 13286.026 3203.035 1093.457 500.000
## 6 2148.1370 14589.4820 3403.3623 1173.9519 500.0225
## 7 1999.8200 27991.6300 4707.5635 1293.5547 502.2908
## 389.1814_5.311 442.19_5.313 325.1288_5.321 369.1737_5.331 344.143_5.362
## 2 12947.3470 13203.1370 2669.9553 15131.5930 3397.2060
## 3 11616.7240 23201.4790 6323.1840 5391.0400 9657.8980
## 4 15315.8430 11578.8180 5276.3696 9676.5610 4878.5250
## 5 10024.668 10516.662 3596.335 3973.209 2631.006
## 6 11237.2730 8790.4640 6082.9517 33698.8050 4509.8070
## 7 58495.2420 7053.8500 89544.2600 6309.5093 51613.2500
## 413.1993_5.357 329.1602_5.369 629.3537_5.37 343.1396_5.368 200.1291_5.38
## 2 20376.2520 24620.9180 4531.7870 14806.8750 19595.5040
## 3 6692.1074 11565.4180 4704.1494 46210.9600 46915.1100
## 4 16209.3120 13950.6860 14028.5840 20233.7400 28769.7640
## 5 6354.866 13071.861 3503.574 10672.633 22697.193
## 6 33519.0040 18615.5210 4097.5747 24811.5210 27645.8100
## 7 3962.4602 72749.5200 6564.5664 248378.9800 130450.0500
## 479.2283_5.383 229.0539_5.383 640.3334_5.395 662.3153_5.383 411.2021_5.398
## 2 58214.7270 2134.5254 1879.2208 500.0201 10346.4270
## 3 40937.7930 121871.5500 21882.2460 6553.6025 13203.2480
## 4 25046.6210 14170.3870 21452.8500 5815.1357 17267.0720
## 5 16035.868 57323.234 1315.734 500.000 9837.673
## 6 21580.6640 13520.7540 2236.6270 500.0225 14340.0320
## 7 11134.7250 105680.1200 42352.7150 9797.3790 32526.9820
## 553.1926_5.396 246.1531_5.399 407.2799_5.411 621.1797_5.389 543.2074_5.421
## 2 500.0201 8996.2290 1028.0500 1070.4548 1085.4550
## 3 502.2117 10460.2750 10389.7130 502.2117 502.2117
## 4 500.2725 20256.6970 1975.2726 500.2725 1076.1653
## 5 500.000 8449.521 14899.009 500.000 500.000
## 6 1102.4485 10757.1520 2966.6746 1057.1858 1269.5510
## 7 502.2908 5103.6797 1206.4402 502.2908 1047.4636
## 391.1968_5.431 463.0831_5.418 603.0611_5.431 467.1919_5.446 193.0353_5.465
## 2 2738.1912 1340.4934 500.0201 15354.9490 3243.8270
## 3 1827.1753 2845.3499 502.2117 5982.7725 3619.2812
## 4 2538.6736 2448.2473 500.2725 9843.9550 3813.4663
## 5 3200.716 1009.453 500.000 15191.840 1430.522
## 6 2528.2039 1852.4631 500.0225 11772.9580 3062.7540
## 7 4559.5090 3426.5671 1396.4355 12405.8830 5620.2670
## 405.1707_5.449 407.129_5.446 474.1054_5.422 535.0762_5.453 528.2617_5.454
## 2 500.0201 500.0201 500.0201 500.0201 4031.6567
## 3 502.2117 502.2117 502.2117 502.2117 2044.1810
## 4 1529.2470 1062.6777 500.2725 500.2725 3067.1716
## 5 500.000 500.000 500.000 500.000 1983.262
## 6 1054.0139 1142.9274 500.0225 500.0225 2681.4048
## 7 1537.0580 1222.5721 1744.0751 502.2908 2752.0050
## 405.118_5.456 421.0582_5.463 319.1396_5.493 305.1604_5.512 367.2123_5.512
## 2 1697.5070 2708.6428 40740.5300 7284.7780 1740.8025
## 3 1438.7695 1310.8369 35223.1100 3532.3486 1125.6466
## 4 1958.4794 500.2725 36789.4650 11569.7450 3451.5405
## 5 1247.273 1365.815 3131.644 5746.319 1330.111
## 6 1524.5052 54073.2600 12863.1970 27119.1170 9470.6220
## 7 1623.6658 1613.3344 28748.2970 34464.7100 5390.8350
## 526.2476_5.551 379.1955_5.602 435.2232_5.611 416.1536_5.683 461.1337_5.706
## 2 18850.8160 500.0201 19744.7190 500.0201 3057.5410
## 3 5487.0000 502.2117 38704.1760 2210.1304 6795.5420
## 4 15333.2500 1054.3843 18302.2050 2178.9585 8785.1960
## 5 4479.613 1295.213 12186.927 3820.612 10571.638
## 6 7234.5356 1333.6758 13620.8870 2163.3333 8601.2820
## 7 24299.5180 80812.1100 53611.0500 2933.4980 7262.4710
## 481.2073_5.68 491.1861_5.71 501.2122_5.698 583.3118_5.69 486.1199_5.689
## 2 25390.5880 1320.0769 500.0201 3827.3604 500.0201
## 3 11387.3350 6180.4980 20087.5780 10774.9480 502.2117
## 4 15662.7650 1049.0277 1673.5681 7042.0723 500.2725
## 5 10571.452 500.000 1145.778 6175.060 500.000
## 6 6319.4450 500.0225 1123.5095 15891.9900 500.0225
## 7 16433.9980 1133.5822 1158.9225 2899.5942 502.2908
## 477.2332_5.701 500.282_5.708 445.1587_5.793 529.121_5.76 594.3642_5.768
## 2 2181.8384 500.0201 3087.3535 1793.9824 6973.2485
## 3 1590.8765 502.2117 2069.0005 6140.8364 70584.6400
## 4 1166.6010 500.2725 2656.8408 6818.5737 12761.5180
## 5 1461.699 39633.395 2540.361 7222.016 25257.908
## 6 2061.0908 500.0225 2481.2893 5701.0360 6704.6210
## 7 16236.4000 502.2908 3431.7617 6164.1626 5536.9473
## 331.2144_5.765 448.1961_5.778 467.1506_5.777 747.3107_5.769 753.328_5.771
## 2 1633.9688 500.0201 2048.3564 500.0201 500.0201
## 3 2371.1074 502.2117 6777.2580 1497.0440 502.2117
## 4 7880.2140 500.2725 10671.2060 1494.2919 1102.9610
## 5 6886.719 500.000 12089.307 500.000 1087.015
## 6 5070.4756 1175.8634 10565.5860 500.0225 500.0225
## 7 6276.5140 502.2908 7449.4600 502.2908 502.2908
## 461.1338_5.769 685.341_5.775 331.1763_5.775 451.125_5.775 399.1634_5.776
## 2 3057.5410 500.0201 31980.7090 3377.0320 5757.4730
## 3 8331.2960 2162.3804 144166.0000 9500.9770 26219.3340
## 4 12213.9810 4053.9753 216570.2800 14970.2290 35131.5100
## 5 11611.581 4324.617 205911.580 15217.675 38359.668
## 6 9252.2280 2833.4746 164455.4700 11973.7050 28834.2560
## 7 10928.1380 2208.0051 185423.0500 11140.2470 28858.0640
## 387.1489_5.77 663.3572_5.772 367.1578_5.796 429.1762_5.798 407.2799_5.805
## 2 500.0201 1118.3784 88616.8050 16817.8890 1725.4916
## 3 502.2117 5551.0910 34362.1950 11833.7160 8467.0000
## 4 500.2725 2482.7812 33942.8240 6238.9644 2241.1953
## 5 500.000 4029.835 10800.803 7995.852 13887.737
## 6 500.0225 2061.7334 186158.0300 4617.9673 10292.0090
## 7 502.2908 2043.8568 27513.7600 18331.1110 1285.9371
## 222.1134_5.811 509.2752_5.821 437.0526_5.832 511.2181_5.843 461.2388_5.842
## 2 20131.9120 12520.9795 7512.7690 6566.1370 11973.6840
## 3 32680.5330 3916.2463 1170.5023 12403.6030 13561.2040
## 4 43111.1950 7785.1226 500.2725 6135.0293 14784.5190
## 5 25701.314 10226.408 2552.115 3394.999 9140.549
## 6 28337.3260 13470.8970 9565.3670 5085.6400 12646.8890
## 7 46498.8360 7646.6860 1519.2506 7645.7837 23317.4900
## 495.2232_5.846 529.162_5.829 345.1553_5.874 467.2645_5.878 497.2751_5.86
## 2 30918.5350 500.0201 23340.3610 23616.4400 2610.3608
## 3 6232.9697 1601.7107 25768.8930 3164.6057 1742.8497
## 4 5922.3140 500.2725 35932.0270 6060.4580 2179.1472
## 5 5351.081 500.000 16837.640 11580.010 1959.350
## 6 2931.7292 1127.5375 23477.2420 12453.0600 4171.2944
## 7 28736.4400 1354.7705 37412.6950 3336.3300 4375.2285
## 613.3589_5.89 379.1393_5.891 463.2334_5.897 475.2184_5.932 565.3012_5.907
## 2 26207.8670 1218.0563 18118.6930 2698.4640 13901.0720
## 3 22226.7520 1165.9767 6107.7056 1337.6556 10938.4000
## 4 57371.4200 500.2725 8941.6560 2093.8506 19063.0720
## 5 19845.783 1433.155 12609.671 500.000 7021.513
## 6 17457.3570 1558.3872 20039.7460 1790.8436 5426.2110
## 7 57183.9770 1505.9944 7462.6730 10889.0030 5859.9060
## 583.3118_5.932 491.228_5.918 567.3168_5.938 528.2633_5.962 550.2453_5.959
## 2 3671.7795 3077.4897 17762.6370 38637.6330 10067.3880
## 3 14698.7050 1077.9639 7784.8010 28933.8360 10546.3580
## 4 5976.6724 1191.0087 13694.3000 50107.0700 12697.3600
## 5 1829.164 1246.042 7636.197 36839.250 8387.270
## 6 9048.5360 3320.2893 6765.6530 31670.9980 7620.1510
## 7 9995.9390 2861.8708 4236.4030 94076.7400 24963.3200
## 509.2379_5.991 443.1557_5.98 465.1373_5.985 446.2908_6.024 369.1552_5.993
## 2 3319.1165 92757.7800 23771.9260 1971.5884 8651.6170
## 3 2286.9940 66802.6700 17166.8770 10073.2000 5791.5340
## 4 3478.7979 33966.9180 10277.8600 7636.6333 10599.6230
## 5 1776.782 45578.850 13733.121 2564.062 8412.393
## 6 3516.8462 39452.7030 11983.0280 3319.9001 9297.9870
## 7 6879.7120 102725.4100 24294.4180 11342.1480 18756.1620
## 345.1552_6.009 579.2778_6.011 511.2909_6.014 512.1916_6.018 624.3385_6.01
## 2 29148.0840 18536.4200 99607.2600 500.0201 9868.4960
## 3 29983.0350 8165.4570 40257.8300 502.2117 5978.2240
## 4 38178.9180 8655.9140 43796.2340 500.2725 24226.7070
## 5 15681.922 13243.390 63861.293 500.000 6894.057
## 6 28462.5490 5080.3413 30582.4180 500.0225 4857.5557
## 7 23003.6020 6157.9990 24339.0020 502.2908 24894.4260
## 476.1913_6.018 417.2126_6.021 544.1789_6.025 496.198_6.048 257.0816_6.056
## 2 500.0201 19204.3420 500.0201 500.0201 12280.1540
## 3 502.2117 17715.6110 502.2117 502.2117 10617.8750
## 4 500.2725 22952.4770 500.2725 500.2725 500.2725
## 5 500.000 10533.724 500.000 500.000 500.000
## 6 500.0225 18213.1100 500.0225 500.0225 500.0225
## 7 502.2908 35303.6330 502.2908 502.2908 502.2908
## 369.1737_6.099 437.1608_6.099 566.3329_6.114 446.2907_6.081 533.2364_6.145
## 2 66615.3100 9016.8660 2603.6562 1971.5884 55716.5230
## 3 41354.3480 6038.2690 2093.7744 8095.7150 15094.4330
## 4 45262.4180 7218.6750 6567.7550 10381.0205 36507.9140
## 5 35725.320 5862.789 2332.630 2564.062 39470.746
## 6 130593.6250 17558.7170 1887.0822 1796.1227 43421.9900
## 7 58683.8870 9514.2690 5535.0146 5062.5370 19414.7320
## 465.2493_6.146 869.3936_6.15 601.2235_6.147 585.1981_6.148 523.2077_6.148
## 2 300830.6600 500.0201 17970.0270 18568.0120 16883.5370
## 3 78652.2700 502.2117 5087.2820 5776.0093 4331.2820
## 4 186971.8800 500.2725 11645.3400 10978.6940 10122.1120
## 5 202456.440 500.000 12246.770 14130.377 12557.078
## 6 231344.1100 500.0225 12881.9290 13683.7150 12175.3000
## 7 116360.9000 502.2908 7134.1900 7617.4150 6948.3306
## 510.2524_6.169 254.6229_6.168 627.3743_6.176 457.2207_6.206 521.239_6.221
## 2 20275.6700 4623.0977 6618.1353 500.0201 1046.2118
## 3 17112.4940 3222.2466 4524.0728 502.2117 502.2117
## 4 44985.6760 7927.3920 7483.6665 1003.8928 1277.0312
## 5 16190.145 3123.695 4299.785 500.000 500.000
## 6 16388.1540 2694.7131 14348.0770 1160.4958 1013.5504
## 7 19043.1820 2504.4514 12548.5650 1079.9790 1331.9034
## 393.2643_6.229 517.1295_6.231 579.0995_6.236 381.1551_6.237 193.0354_6.242
## 2 7018.8040 500.0201 500.0201 2396.8450 1207.0001
## 3 5735.1610 502.2117 502.2117 1200.4326 1142.7527
## 4 12394.6400 500.2725 500.2725 1279.8898 500.2725
## 5 3846.231 500.000 500.000 1057.665 500.000
## 6 9533.6540 500.0225 500.0225 3174.2563 1533.3323
## 7 9603.9080 1268.0364 502.2908 4781.7800 1299.9011
## 381.1937_6.247 439.1146_6.219 501.1047_6.238 449.142_6.234 511.1128_6.237
## 2 1244.7769 500.0201 500.0201 1281.9812 500.0201
## 3 1381.1210 502.2117 502.2117 1067.3020 502.2117
## 4 1012.2658 1008.9587 500.2725 500.2725 500.2725
## 5 1185.178 500.000 500.000 500.000 500.000
## 6 1746.7676 1056.6683 500.0225 1131.5266 1022.5649
## 7 1270.5898 1045.2396 1060.7882 1961.8164 502.2908
## 405.2644_6.291 573.2395_6.305 471.2417_6.297 307.1184_6.294 509.2751_6.332
## 2 1387.9204 500.0201 18684.4730 6102.5850 21149.9590
## 3 3724.9272 1008.0635 18294.3600 1373.2618 4311.4510
## 4 1505.1262 500.2725 23933.8360 1323.8584 11959.6020
## 5 2287.129 1017.574 7752.073 2922.535 20269.045
## 6 11140.8090 500.0225 6665.7790 24614.3240 11147.6880
## 7 2388.3218 5741.0110 35014.1400 20257.7600 3102.0410
## 441.2108_6.348 419.2281_6.362 324.0682_6.363 280.0779_6.362 489.27_6.391
## 2 4303.2110 11611.4080 500.0201 500.0201 19273.8750
## 3 3087.0044 7739.5160 502.2117 502.2117 15949.5800
## 4 3277.2034 13111.4810 500.2725 500.2725 8301.6480
## 5 2454.044 8878.062 500.000 500.000 8016.592
## 6 2949.6184 4228.3320 500.0225 500.0225 12656.2660
## 7 5087.9450 14919.4230 1066.9482 502.2908 30728.7660
## 448.3058_6.47 495.296_6.494 623.2016_6.501 613.1932_6.507 551.2033_6.504
## 2 17199.8050 27995.4380 1837.4498 1894.7000 1654.2815
## 3 7255.5576 8289.5670 1504.4625 1087.0563 1042.4944
## 4 13846.4050 19739.8570 500.2725 500.2725 500.2725
## 5 12527.042 18634.680 500.000 500.000 500.000
## 6 18120.8960 12107.6045 1008.3889 500.0225 500.0225
## 7 3281.5308 18387.5060 1411.7673 1726.5679 1590.9014
## 629.2176_6.511 591.2109_6.51 493.2439_6.516 561.231_6.512 691.188_6.507
## 2 2108.0002 1857.5790 18997.6820 4671.8643 1491.9589
## 3 1077.4749 1005.4697 9637.5660 2719.0767 1057.6036
## 4 500.2725 500.2725 1452.6182 500.2725 500.2725
## 5 500.000 500.000 2134.265 500.000 1014.569
## 6 1305.1003 1174.1191 4077.1365 1140.4529 500.0225
## 7 1675.7294 1469.7109 12909.8480 2969.5227 1273.0312
## 493.2869_6.516 513.2714_6.619 489.27_6.555 514.2774_6.559 446.2909_6.562
## 2 1569.7882 12710.6820 22514.0470 16738.1210 70527.0900
## 3 1346.8307 5424.4270 11159.2440 5405.3520 23826.0550
## 4 1046.6996 4672.1820 8618.8390 6591.5410 28002.0590
## 5 500.000 3951.774 10743.843 3153.488 10707.768
## 6 500.0225 7240.8696 9633.0790 7264.9170 24641.2770
## 7 1055.0084 13957.7940 22765.8870 11843.5540 46801.4600
## 534.2499_6.614 512.2682_6.624 567.317_6.697 491.2855_6.773 407.2801_6.791
## 2 9309.4710 39989.2150 3238.9160 4670.7725 2874.2740
## 3 3499.2440 12448.2730 9105.0490 8206.8880 1008.4620
## 4 4768.9470 16551.2170 11183.8330 4406.3440 4265.1880
## 5 2497.870 10731.489 2711.477 3076.349 1270.337
## 6 5315.4844 19152.4120 3992.4788 2627.6377 44685.7100
## 7 8661.4000 39859.5040 12001.4920 11399.5260 1092.4450
## 507.2231_6.98 505.2452_7.002 267.1235_7.063 437.18_7.078 573.2393_7.26
## 2 4173.1970 500.0201 34378.4140 1187.5945 500.0201
## 3 25650.5760 502.2117 23522.3050 1160.8503 502.2117
## 4 22368.4530 1112.6277 9432.8070 500.2725 500.2725
## 5 32762.258 500.000 87857.734 500.000 1037.959
## 6 24798.7380 500.0225 58489.6200 6081.6685 500.0225
## 7 19402.6580 502.2908 53023.7800 1701.7600 3339.9758
## 673.2558_7.365 519.223_7.484 557.2465_7.479 517.2413_8.045 449.254_8.044
## 2 500.0201 1176.9064 1107.4280 20837.4790 116360.6000
## 3 502.2117 502.2117 1055.2023 1358.1008 6277.5054
## 4 500.2725 1026.7890 500.2725 12336.5550 62160.6500
## 5 500.000 1087.832 500.000 14514.675 76166.440
## 6 500.0225 1020.6257 500.0225 20214.2680 113360.1300
## 7 5379.2450 502.2908 2644.6428 1239.2578 3941.5579# change to results to numeric
data_notame <- data_notame %>%
mutate_at(-1, as.numeric)
head(data_notame)## mz_rt 236.065_0.603 168.0777_0.606 154.0621_0.609 124.0072_0.616
## 2 6111_U4_C18NEG_42 4148.156 6707.072 304457.60 63058.53
## 3 6101_U2_C18NEG_30 65118.800 81841.780 112404.43 22063.85
## 4 6102_U1_C18NEG_26 16741.850 29358.371 168966.02 56236.64
## 5 6110_U2_C18NEG_34 16350.678 19073.940 67706.22 22557.04
## 6 6105_U4_C18NEG_47 46170.508 59856.336 208992.60 56521.90
## 7 6103_U4_C18NEG_53 62220.047 90745.414 92892.30 4167.15
## 193.0351_0.617 239.1144_0.622 275.056_0.654 195.051_0.627 215.0326_0.629
## 2 110661.23 4148.840 7621.462 175922.42 83845.84
## 3 50736.00 13944.503 10538.454 222889.05 112802.20
## 4 54822.62 13815.239 14388.471 185187.05 93607.89
## 5 27333.80 2379.748 12803.501 97982.84 72868.77
## 6 51904.73 13813.590 54594.406 119151.62 87575.54
## 7 81048.39 7217.615 11000.114 109782.73 56883.41
## 219.0457_0.629 217.0486_0.63 245.0433_0.63 165.0405_0.634 187.0383_0.634
## 2 14710.47 42995.40 22739.150 84372.05 17612.54
## 3 21731.16 67756.38 13593.999 129945.73 16133.58
## 4 16508.49 46851.04 11583.424 62910.26 16036.78
## 5 29380.19 84598.01 9735.333 99338.50 14608.52
## 6 18316.07 49151.78 15733.052 81935.97 21083.07
## 7 25524.66 80661.06 13465.152 79159.32 22429.19
## 265.0732_0.64 235.057_0.641 135.0295_0.64 277.0852_0.658 179.0556_0.643
## 2 3554.009 10549.707 215612.5 1896.890 49858.58
## 3 2770.605 7714.571 161603.8 1777.623 69490.11
## 4 3590.480 13949.080 180550.1 1650.315 54854.73
## 5 2833.232 2971.846 118229.0 1515.141 79040.13
## 6 2556.381 5410.374 180194.7 1872.541 66115.34
## 7 1964.567 4466.612 110849.0 1644.784 34922.00
## 149.0089_0.649 547.1106_0.67 149.0452_0.654 262.0364_0.654 321.0625_0.656
## 2 59227.816 1480.111 53766.40 24966.15 10882.572
## 3 94413.370 1979.919 55440.19 16337.39 25443.610
## 4 8282.051 2248.388 50711.70 22093.76 18592.223
## 5 14616.016 1228.672 64062.99 35391.87 8272.857
## 6 3099.258 1445.831 53487.29 18773.77 11248.721
## 7 8325.330 1888.409 32421.29 79602.77 6367.673
## 174.0405_0.655 291.0683_0.654 167.0208_0.657 261.039_0.66 494.1144_0.666
## 2 21986.17 3920.858 391255.3 21221.85 1378.356
## 3 22722.28 3144.512 352507.2 33641.87 1788.408
## 4 22790.85 2798.266 404390.7 20416.87 1299.250
## 5 16850.51 4256.903 614587.0 31758.99 2543.849
## 6 39613.52 3506.388 847425.0 20707.12 111281.990
## 7 16621.40 3902.843 378931.6 55393.78 1669.535
## 308.0983_0.658 247.062_0.659 151.0066_0.66 363.0756_0.658 277.052_0.659
## 2 17307.66 25386.652 8439.765 11992.52 8896.553
## 3 15982.89 19120.885 11509.525 37390.39 5537.230
## 4 21815.36 39358.438 12856.635 36373.70 8356.729
## 5 11636.19 16349.409 18192.979 21776.12 12400.021
## 6 15389.84 27319.941 10566.316 19312.00 7137.539
## 7 10550.26 6860.665 17224.197 12659.90 7859.851
## 262.0286_0.664 287.0195_0.66 290.0877_0.662 287.0526_0.66 263.0573_0.662
## 2 21046.44 14930.026 8933.062 7753.760 15739.020
## 3 33996.13 7956.347 4929.507 7766.633 17729.266
## 4 27414.63 9422.424 8266.643 8011.123 17002.371
## 5 33476.04 14959.646 6859.244 5627.218 11740.802
## 6 22452.06 10694.958 9186.924 6865.495 17202.941
## 7 27361.56 14248.406 4465.655 5874.456 6983.899
## 253.9903_0.661 188.0021_0.665 337.0436_0.684 300.039_0.661 461.0646_0.667
## 2 11407.440 18472.05 500.0201 22658.52 1432.187
## 3 7760.920 56319.30 4015.7778 24527.46 3294.442
## 4 11899.625 12348.45 4727.2830 27361.35 2893.552
## 5 8558.486 17131.70 3151.2400 12541.50 1607.297
## 6 9068.235 20309.28 2994.2952 32732.29 25902.227
## 7 11395.034 10298.02 3248.6460 13565.71 1928.012
## 463.0657_0.669 295.0277_0.684 379.0686_0.661 491.0719_0.671 247.9959_0.702
## 2 1728.165 3877.864 19467.22 1976.401 9789.628
## 3 3113.431 3143.347 126441.66 1952.864 26527.518
## 4 2402.470 4319.640 77050.38 1530.923 9697.385
## 5 1732.253 2029.218 27472.50 1314.838 11553.704
## 6 3606.900 3085.359 23418.00 2655.319 7443.909
## 7 2762.229 2343.916 23161.10 1687.461 6933.913
## 243.0617_0.662 351.0566_0.663 373.0567_0.663 557.1344_0.671 351.0199_0.667
## 2 30853.54 32206.31 7283.209 1498.143 3787.962
## 3 23710.47 19565.97 11812.051 1949.107 15992.729
## 4 34791.48 32150.52 10058.436 1646.734 18490.943
## 5 29982.63 14812.69 3590.756 1011.158 6557.495
## 6 31734.12 32706.41 5388.302 5968.790 4365.383
## 7 22360.31 14061.23 4312.708 1533.398 9119.174
## 323.0417_0.656 396.025_0.663 293.0301_0.685 254.9816_0.664 389.0688_0.663
## 2 4656.880 36380.03 4546.778 72849.41 10816.223
## 3 5719.677 32557.80 7261.963 78899.58 26806.127
## 4 2628.112 22872.48 6610.026 112633.54 24806.818
## 5 3849.919 9591.22 2736.389 38314.09 8937.354
## 6 6543.930 21491.27 34597.047 60320.02 12854.002
## 7 2698.003 14241.13 11340.430 33674.39 5623.102
## 328.0474_0.665 275.0569_0.663 445.0903_0.667 289.0702_0.663 293.0676_0.65
## 2 7255.310 11471.70 8148.391 9563.983 10029.273
## 3 6964.973 10538.45 18728.790 6446.380 14552.439
## 4 7752.760 14388.47 18968.030 9187.526 10567.366
## 5 4605.590 12803.50 4771.278 8563.401 10739.345
## 6 6006.595 54594.41 10714.825 7585.174 12973.740
## 7 4763.804 11000.11 7042.906 4399.026 8009.932
## 541.1207_0.663 227.9967_0.668 395.0628_0.675 405.0648_0.668 398.0412_0.681
## 2 3575.813 72074.09 12726.30 6694.696 2889.994
## 3 10819.161 72658.74 53118.33 34385.707 2812.615
## 4 8255.391 45834.60 31870.00 23018.111 3777.036
## 5 2170.591 19956.22 15352.70 8227.380 8131.128
## 6 5221.691 34732.79 15376.13 10411.945 268753.470
## 7 15615.923 26029.39 23932.92 7877.498 4579.863
## 231.0308_0.666 289.0528_0.664 375.054_0.665 225.0623_0.658 360.0729_0.672
## 2 12636.01 9199.498 8978.262 3959.293 9899.389
## 3 24950.57 6697.975 29421.720 7518.006 31006.035
## 4 23874.47 7208.151 20557.312 23502.887 24456.684
## 5 11439.28 7413.016 9835.665 2361.148 8538.419
## 6 18868.81 7997.809 9912.558 17117.385 16410.525
## 7 13173.44 4967.005 6042.798 8349.902 6997.918
## 216.0196_0.667 261.0232_0.668 260.0254_0.667 347.059_0.674 307.0116_0.682
## 2 58641.75 173269.6 117672.6 10468.38 1521.851
## 3 178319.86 340208.4 203477.6 30192.11 2271.590
## 4 157426.64 316508.0 211304.9 22496.08 2083.192
## 5 69920.41 273429.5 165951.3 23684.22 2951.281
## 6 80708.16 229276.5 170327.8 30482.32 5467.719
## 7 124550.24 317140.1 192901.4 22400.62 6189.201
## 356.031_0.667 129.0191_0.669 331.0274_0.668 85.0291_0.668 345.0438_0.668
## 2 6124.045 236005.1 21954.35 84031.93 37873.72
## 3 18968.357 329562.7 19095.89 110775.65 72203.16
## 4 4514.106 312758.8 18446.11 116002.26 108276.02
## 5 6339.006 229952.8 24770.73 83288.96 26113.56
## 6 4446.531 295936.3 12468.62 108941.45 40104.81
## 7 7339.775 297184.9 40800.06 91490.17 15130.20
## 336.0493_0.662 217.0162_0.671 349.059_0.669 371.0241_0.671 359.0566_0.67
## 2 21169.90 254774.4 26902.29 3997.847 11019.816
## 3 20621.57 666478.4 74113.73 10361.553 15147.177
## 4 20894.64 575733.5 41118.16 9828.128 17454.236
## 5 41425.48 245340.3 27493.34 2132.876 8432.428
## 6 50127.76 298069.7 27595.81 2971.794 11856.248
## 7 21760.66 460064.9 19456.70 5593.301 9593.075
## 475.0782_0.667 432.2024_0.669 246.0075_0.694 304.0334_0.669 305.0302_0.67
## 2 2270.458 500.0201 3329.670 104681.8 147793.8
## 3 4959.023 502.2117 3286.561 137143.0 184741.4
## 4 4681.269 500.2725 2274.452 153807.9 189132.4
## 5 2710.678 500.0000 5420.414 136424.4 180358.3
## 6 2697.789 2117.2908 131341.420 107177.9 153268.8
## 7 3094.526 502.2908 2874.550 163900.8 203005.8
## 431.0534_0.666 274.0026_0.671 230.0493_0.666 550.1307_0.664 173.0089_0.672
## 2 5363.149 7800.160 2385.206 1647.118 387825.8
## 3 11396.476 56630.137 2519.885 2237.812 498700.2
## 4 8234.119 6476.700 2632.771 2314.856 448936.1
## 5 4677.412 16356.477 2170.819 1977.353 354741.7
## 6 10177.327 8834.401 24179.092 1834.480 437558.3
## 7 5358.130 28521.215 2795.769 1410.586 458486.2
## 526.0995_0.675 232.005_0.705 330.0291_0.673 410.0417_0.684 301.0547_0.665
## 2 1524.602 4428.511 21943.51 23547.430 4664.002
## 3 1872.984 6473.889 19465.50 12721.896 3882.189
## 4 1563.827 4951.872 17778.10 2655.240 4994.150
## 5 2444.157 3688.011 33552.07 2591.827 3447.831
## 6 141841.330 48300.520 17316.74 5945.292 29855.465
## 7 1605.157 2360.280 46560.23 5217.218 17143.390
## 274.0396_0.676 691.1349_0.67 247.9921_0.704 318.064_0.674 315.0338_0.673
## 2 12929.94 1078.6835 9789.628 7785.817 8878.005
## 3 12111.82 502.2117 26527.518 10388.058 18749.488
## 4 11576.77 1015.5614 9697.385 10731.376 22687.920
## 5 12632.41 1233.0360 11697.337 8062.857 13496.725
## 6 13121.78 2851.9258 4977.282 7053.570 11470.785
## 7 10873.22 1045.9828 7218.643 5680.635 12024.852
## 320.0597_0.683 277.0349_0.667 338.1134_0.651 462.0712_0.669 262.9863_0.693
## 2 28297.39 4416.891 1313.618 1579.116 2423.988
## 3 30200.49 9214.732 1212.621 3230.344 3717.879
## 4 21522.02 4337.562 1055.631 2531.174 2146.259
## 5 12583.48 4946.517 1185.092 1835.917 3136.109
## 6 15244.03 5060.556 1195.891 6311.497 4296.731
## 7 13343.08 3963.088 1609.490 1764.891 1933.574
## 264.9884_0.68 399.0203_0.683 344.0448_0.677 303.0365_0.684 365.0528_0.68
## 2 113964.42 6803.435 24700.07 28172.19 9602.565
## 3 112022.44 33700.070 50612.17 37815.97 33895.285
## 4 131204.67 27662.969 58156.92 56812.63 11426.093
## 5 57290.05 19677.950 17857.01 38021.71 20620.957
## 6 154751.20 12679.599 29855.48 48059.88 13932.004
## 7 95716.98 37637.440 17081.19 42261.23 10952.647
## 307.0326_0.669 246.0433_0.669 275.0216_0.69 288.0231_0.685 278.0698_0.683
## 2 7651.126 6301.940 21675.70 2171.035 11119.688
## 3 12801.028 4495.199 48333.08 2594.452 12375.687
## 4 9367.340 5753.604 46051.78 3280.475 9924.608
## 5 10809.471 6208.385 27149.67 7146.336 12335.614
## 6 6298.244 9574.203 52112.79 2866.753 8318.396
## 7 13549.310 6263.126 27632.22 6015.031 10469.758
## 218.9961_0.686 288.0398_0.684 417.0297_0.684 191.0212_0.699 509.0384_0.699
## 2 29057.56 5254.134 36776.48 4271292 9943.6940
## 3 26525.75 14368.870 74917.35 5971350 14769.3430
## 4 17512.94 12189.894 71361.46 5661172 500.2725
## 5 69096.27 6233.876 46809.65 5800687 1152.2520
## 6 49340.29 11836.968 45453.77 6219833 500.0225
## 7 45610.52 8530.920 80406.51 5948256 1457.8425
## 265.0186_0.67 289.0363_0.688 300.0195_0.687 661.1248_0.679 436.092_0.669
## 2 5677.824 21162.40 6127.32 500.0201 1625.533
## 3 5042.470 38123.38 16401.19 1504.8782 2302.190
## 4 5974.380 38736.93 17186.10 500.2725 2267.769
## 5 9163.784 25467.67 14968.06 500.0000 500.000
## 6 10135.686 32850.72 10955.58 1238.6486 1547.042
## 7 9497.146 29641.80 29918.47 1207.5732 6623.473
## 480.0818_0.682 419.1179_0.71 361.0578_0.686 242.0115_0.695 385.0951_0.677
## 2 1683.972 4461.071 22139.16 76768.000 1877.189
## 3 2538.070 5500.953 37330.00 36109.594 1451.558
## 4 2445.187 3409.887 37392.11 3506.001 1697.665
## 5 1214.369 2178.181 13581.20 4123.630 1439.647
## 6 3476.852 5135.953 37359.74 6403.408 1721.645
## 7 1833.886 4428.649 22033.45 4559.387 1516.486
## 487.0783_0.683 145.0141_0.693 631.1154_0.686 302.0532_0.694 439.056_0.753
## 2 2089.931 32694.46 1107.0416 19273.21 3057.648
## 3 3229.205 27103.39 502.2117 17813.26 2580.377
## 4 1752.802 39290.84 1138.2074 25512.56 1636.482
## 5 1377.679 18083.13 500.0000 11645.04 1544.656
## 6 4552.856 29402.82 1328.6703 18164.51 2300.604
## 7 2126.688 48293.88 1027.8997 12906.65 3150.193
## 418.1123_0.717 377.0526_0.697 296.9824_0.699 218.0492_0.684 332.0663_0.696
## 2 9240.758 16165.51 172357.0 6156.410 14352.75
## 3 27600.758 22953.60 261831.1 86759.540 11513.19
## 4 8733.800 17618.71 260996.6 9987.960 14640.16
## 5 10812.652 18719.19 141970.6 20545.275 13763.93
## 6 20853.950 24687.98 211734.0 9220.207 11579.38
## 7 9241.500 10513.12 254820.1 32551.074 10625.55
## 133.0504_0.696 405.0469_0.697 349.0207_0.696 295.9856_0.697 527.1244_0.676
## 2 58184.89 12246.46 9556.484 44239.14 1520.082
## 3 19859.45 20985.31 21081.809 68629.15 1453.922
## 4 26414.59 20242.28 2572.469 71051.29 1478.560
## 5 39528.96 22405.70 2777.294 36044.31 3086.462
## 6 27557.38 21118.88 6357.444 65073.44 6357.520
## 7 40625.38 17682.35 1964.025 64934.23 1952.469
## 333.0634_0.694 392.0363_0.698 570.0904_0.696 394.06_0.685 144.03_0.697
## 2 50376.02 79644.32 1192.527 5016.383 20549.54
## 3 37198.00 211934.05 1945.405 17367.436 12359.06
## 4 45505.54 195954.06 1329.654 10407.042 17822.94
## 5 36155.80 111624.73 1486.894 11816.269 14422.31
## 6 44392.86 216734.20 1282.268 6937.322 18463.63
## 7 27413.29 155174.64 4704.697 47895.934 12584.45
## 391.0332_0.698 296.0197_0.697 191.0523_0.699 319.0474_0.696 347.0424_0.697
## 2 493382.8 2065.585 128032.3 228269.97 21344.03
## 3 1598408.4 1706.899 127600.2 207429.16 76096.59
## 4 1336129.4 2461.055 114005.6 330992.10 35181.47
## 5 737906.1 1892.521 113870.6 140869.95 32082.86
## 6 1615176.4 1539.014 104292.6 204233.78 27753.13
## 7 654401.1 2343.535 148865.5 66676.64 25022.49
## 316.0285_0.688 191.0203_0.697 192.0224_0.702 320.05_0.698 397.0582_0.701
## 2 2335.339 4271292 244912.1 32589.74 17310.68
## 3 17480.080 5971350 425455.5 30232.38 23589.91
## 4 2175.916 5661172 382120.7 42970.62 27858.78
## 5 3997.198 5800687 510942.7 19847.18 12179.88
## 6 2973.057 6219833 455817.3 31299.69 20153.56
## 7 5113.091 5006305 402905.9 11344.95 14181.07
## 277.0021_0.707 206.9961_0.7 78.9585_0.702 468.0451_0.696 389.0528_0.7
## 2 5372.949 52142.77 211444.4 1784.077 26811.81
## 3 10250.444 135124.94 170654.6 3806.712 41203.88
## 4 3510.574 62368.05 219096.5 1811.375 37091.09
## 5 2991.485 68935.34 103807.1 5802.637 14793.73
## 6 12172.925 78885.26 189029.5 3278.921 24250.27
## 7 4614.704 83022.69 134074.3 11802.083 21201.19
## 246.9913_0.705 161.988_0.685 154.9984_0.703 246.9916_0.705 111.0085_0.703
## 2 81910.30 500.0201 29219.25 81910.30 406520.8
## 3 229988.02 367110.0000 25145.98 229988.02 661080.1
## 4 73131.02 1071.1361 22682.88 73131.02 603447.8
## 5 85781.72 9216.2580 15626.09 85781.72 626823.2
## 6 32668.59 3869.4077 21313.97 32668.59 730377.2
## 7 41336.69 502.2908 15210.55 41336.69 634365.5
## 216.981_0.709 524.0845_0.705 427.0674_0.685 379.0336_0.708 367.0282_0.707
## 2 60342.52 6745.012 2228.570 15370.52 23634.73
## 3 105255.52 6333.323 2495.810 43376.43 15567.30
## 4 42331.01 5804.370 2213.417 19405.04 15214.25
## 5 48391.12 1876.439 2423.096 12354.70 18005.93
## 6 127584.33 6225.265 4249.378 14402.41 17370.80
## 7 101494.09 17141.700 3508.205 23399.62 7441.63
## 543.0792_0.704 303.0532_0.705 225.0424_0.708 232.9759_0.708 423.0571_0.703
## 2 9441.306 33108.57 1438.579 33282.68 6167.018
## 3 3902.865 45647.30 1193.774 69228.01 44044.965
## 4 6051.264 66692.70 3760.603 18993.38 12221.240
## 5 3249.885 41528.29 1397.930 41496.67 8118.665
## 6 2081.135 45342.85 1462.196 23995.37 45888.895
## 7 7330.236 37758.77 3271.262 36132.21 18807.643
## 347.079_0.706 146.0456_0.708 335.0468_0.663 409.0428_0.71 559.1102_0.68
## 2 28352.67 43757.48 41948.97 5476.098 2089.940
## 3 33714.11 40495.89 150743.64 29388.186 2526.582
## 4 31311.31 32793.04 156294.39 6958.390 3685.181
## 5 29898.60 29624.33 300009.06 7829.560 1578.039
## 6 27567.22 58301.59 55818.12 30897.658 18702.752
## 7 20342.19 34810.22 29560.63 12070.757 2207.556
## 259.057_0.712 377.0353_0.713 263.0249_0.69 370.0786_0.733 499.0756_0.705
## 2 20013.455 19127.55 16781.05 3990.016 1729.873
## 3 7821.584 47368.90 22301.06 3680.261 1900.208
## 4 16123.069 43134.07 19644.12 3496.406 1986.467
## 5 8619.797 27326.04 25428.93 2746.170 3551.452
## 6 13171.827 31398.59 150805.66 3453.331 2036.747
## 7 5871.999 24756.85 11801.44 20682.420 3225.766
## 310.9761_0.719 291.0345_0.717 576.1384_0.705 424.0493_0.721 479.0535_0.737
## 2 55626.69 22876.67 500.0201 23092.508 1715.251
## 3 73050.55 31448.65 1191.1466 18950.043 1446.025
## 4 86704.84 29264.34 1416.7020 10549.774 2122.061
## 5 102870.61 36290.96 1044.0050 8377.034 1198.108
## 6 77251.23 30482.21 2239.2495 19477.725 15126.667
## 7 63273.56 18923.06 1260.1454 9328.327 1831.159
## 405.0285_0.72 230.0139_0.763 418.1149_0.722 303.0715_0.727 258.0424_0.699
## 2 34730.88 26786.172 9386.856 25215.32 11518.438
## 3 96463.87 173433.340 27600.758 30536.90 7196.173
## 4 66619.45 7272.182 19513.600 19851.10 9132.303
## 5 213321.17 31827.668 10812.652 31402.52 9889.745
## 6 91735.66 1094556.000 20853.950 40252.75 6244.516
## 7 129526.12 11000.619 9241.500 44833.55 6408.332
## 549.0364_0.757 485.0955_0.681 303.0712_0.728 584.0883_0.697 573.1087_0.695
## 2 1020.5068 1766.916 25215.32 1191.607 500.0201
## 3 1264.0416 1969.423 30536.90 1020.028 1564.3948
## 4 500.2725 2078.967 19851.10 1133.779 1315.0110
## 5 1164.9730 1888.713 31402.52 500.000 1212.0080
## 6 1231.2000 2009.342 40252.75 1079.573 5382.2840
## 7 502.2908 1626.033 44833.55 2581.037 1605.2543
## 422.0501_0.727 218.1032_0.748 247.0277_0.755 175.0241_0.753 194.046_0.798
## 2 8559.401 13785.02 11656.164 18934.527 85857.49
## 3 6211.201 19853.60 18421.383 6923.810 266435.78
## 4 4910.769 13012.57 7439.392 5896.429 123845.42
## 5 3516.970 11549.19 24980.500 9438.662 212657.14
## 6 11584.159 19291.59 12509.654 23864.926 100885.55
## 7 13048.554 14139.41 40544.082 16835.719 788230.60
## 137.0248_0.733 191.0524_0.713 205.0352_0.76 343.0666_0.756 457.0689_0.726
## 2 3329.118 128032.3 73709.00 23513.518 2072.705
## 3 4851.530 127600.2 39592.65 9829.716 1468.569
## 4 2741.812 114005.6 47546.77 9161.826 1876.003
## 5 7323.842 113870.6 44800.91 4856.681 1510.607
## 6 5956.785 104292.6 87591.40 3503.153 14086.122
## 7 13008.805 126887.2 49713.25 19257.445 2431.969
## 147.0298_0.76 191.0267_0.755 389.1038_0.786 161.0454_0.768 855.1239_0.785
## 2 95357.31 35133.37 1946.280 57661.74 500.0201
## 3 135842.72 34729.11 20584.535 53200.08 502.2117
## 4 70525.44 34061.31 2451.440 53321.21 500.2725
## 5 93652.48 35948.69 5237.721 50538.83 500.0000
## 6 72001.38 32916.34 17286.379 49353.99 1048.2848
## 7 86855.19 41353.94 44659.992 62155.12 502.2908
## 541.1395_0.742 446.038_0.779 177.0225_0.776 324.0723_0.776 208.0646_0.766
## 2 500.0201 1658.710 40995.26 68043.33 8426.654
## 3 1242.6123 2290.653 46588.39 57325.23 8188.783
## 4 1000.5450 1217.450 47735.32 34703.08 4456.158
## 5 500.0000 2121.245 39499.20 22483.91 2965.903
## 6 2088.4001 38099.400 43068.27 42821.12 6748.762
## 7 1661.0409 2480.765 39971.66 163982.88 5671.883
## 757.1576_0.789 301.0563_0.774 1153.2827_0.789 144.0665_0.781 645.1108_0.776
## 2 500.0201 6962.695 500.0201 51786.94 1206.7730
## 3 502.2117 6924.001 502.2117 34476.71 1019.9524
## 4 500.2725 4752.056 500.2725 42257.63 500.2725
## 5 500.0000 2565.265 500.0000 28798.98 500.0000
## 6 1481.4994 29855.465 500.0225 35803.42 1585.2367
## 7 1157.0000 14510.417 502.2908 27630.07 1248.0000
## 165.0409_0.76 555.1553_0.783 245.1137_0.783 299.1247_0.784 364.0954_0.768
## 2 19419.22 500.0201 13970.721 8509.224 1520.592
## 3 17942.37 1373.9762 30608.924 12404.673 1945.422
## 4 28789.53 500.2725 14505.489 9157.642 1601.935
## 5 17728.72 500.0000 6751.876 11656.090 1604.762
## 6 19712.34 2497.3308 20732.162 11879.697 1327.767
## 7 31254.99 1189.3865 20329.336 14583.196 2468.376
## 357.0823_0.779 741.1865_0.791 195.0512_0.787 360.1406_0.756 315.0713_0.751
## 2 7354.266 500.0201 29134.76 2263.692 3304.153
## 3 4200.445 502.2117 47263.91 2437.948 2395.372
## 4 6396.362 500.2725 73776.23 2458.704 2043.200
## 5 7101.456 500.0000 30895.54 1365.453 2464.498
## 6 4792.830 2534.9316 70425.29 2573.952 2448.085
## 7 34339.930 502.2908 215220.38 1706.795 4512.069
## 319.1032_0.789 274.039_0.769 275.0221_0.754 359.0971_0.789 204.9812_0.795
## 2 22483.98 18499.63 10979.95 2741.616 53297.82
## 3 11512.69 11861.78 24789.88 1889.988 41683.94
## 4 14819.93 14183.16 32565.01 2321.969 70088.27
## 5 17165.52 11632.62 24247.92 1814.458 51446.68
## 6 17031.85 11086.06 20019.98 168268.170 104181.96
## 7 25862.67 13121.26 16547.59 4055.086 110612.68
## 216.9808_0.715 771.1962_0.744 231.9926_0.79 356.0987_0.796 261.0072_0.769
## 2 60342.52 1552.0686 11582.256 2576.593 30946.57
## 3 105255.52 502.2117 47461.793 1573.080 68774.08
## 4 42331.01 500.2725 25121.129 3326.744 43543.43
## 5 48391.12 500.0000 17798.205 1769.738 47227.62
## 6 127584.33 1172.4828 13655.878 10235.521 27382.33
## 7 101494.09 1336.9645 5845.785 2619.397 171906.81
## 391.1124_0.79 194.0546_0.797 227.9971_0.795 502.2071_0.806 415.1607_0.797
## 2 2238.778 8675.968 66358.00 500.0201 2236.255
## 3 2061.178 18655.820 81859.79 502.2117 6679.469
## 4 7646.023 9949.765 50406.92 500.2725 2686.223
## 5 1121.088 15096.397 15420.12 500.0000 2700.322
## 6 10854.772 8582.582 30611.09 500.0225 2364.138
## 7 36579.460 38545.450 44149.20 502.2908 2757.714
## 361.1497_0.795 303.0177_0.795 223.0623_0.802 189.9895_0.816 375.1294_0.798
## 2 4447.724 7302.100 1144.658 10736.895 7363.248
## 3 21850.850 14427.292 1835.333 19163.754 28331.732
## 4 7930.003 11333.239 1118.653 13191.913 14353.000
## 5 3655.935 8702.116 1202.347 25967.732 4386.368
## 6 16740.793 42028.562 3526.491 9589.386 18520.574
## 7 32214.530 36042.926 2448.069 19991.594 53226.594
## 276.0018_0.763 168.0301_0.797 399.0923_0.803 373.0779_0.78 227.1036_0.798
## 2 1157.864 36358.31 2278.414 2605.317 12904.631
## 3 3154.721 50863.81 2017.672 1747.880 25581.787
## 4 2522.970 48530.44 2583.001 3284.872 15256.974
## 5 9869.246 61832.36 2220.696 1572.836 7753.059
## 6 44971.043 39271.98 5329.550 5729.190 16874.271
## 7 3434.575 43985.10 6257.025 6013.580 21659.885
## 479.0506_0.791 411.0787_0.764 326.0866_0.798 579.0457_0.796 194.0459_0.799
## 2 500.0201 4575.452 8678.553 1057.334 85857.49
## 3 1241.6469 4025.590 20011.016 1393.800 266435.78
## 4 1161.4454 1995.375 1661.007 1024.116 123845.42
## 5 1198.1080 3552.362 14005.729 500.000 212657.14
## 6 15126.6670 2133.098 524976.250 1327.663 100885.55
## 7 1831.1595 30593.572 5637.061 1434.307 788230.60
## 861.1404_0.799 1123.2723_0.775 516.2234_0.803 230.9967_0.757 282.0473_0.802
## 2 500.0201 500.0201 500.0201 26454.28 5276.602
## 3 502.2117 502.2117 502.2117 48949.51 43431.773
## 4 500.2725 500.2725 500.2725 37910.18 10161.483
## 5 500.0000 500.0000 500.0000 30236.19 17003.963
## 6 1474.1422 500.0225 500.0225 28247.52 34313.310
## 7 502.2908 1337.8524 502.2908 34517.13 25865.146
## 182.0076_0.788 292.0225_0.799 258.9916_0.797 182.0282_0.77 219.0508_0.792
## 2 1016.8692 15912.06 9436.893 500.0201 2348.045
## 3 1093.6797 38215.83 27677.531 502.2117 9981.265
## 4 1148.9729 15948.46 8003.412 3617.5800 2965.590
## 5 1069.3060 19340.38 21220.290 1865.3220 24056.643
## 6 17604.9840 11497.80 15724.651 57946.8830 2706.570
## 7 502.2908 52938.43 13266.907 4012.2197 482739.400
## 182.0458_0.803 489.0926_0.758 337.0556_0.777 389.1018_0.792 371.0976_0.801
## 2 38961.17 1434.736 77891.110 2451.103 6994.354
## 3 51169.77 1411.704 2123.831 20584.535 3121.879
## 4 33758.14 1341.124 3934.707 2709.416 7716.414
## 5 25916.16 1399.844 5028.712 5237.721 5879.060
## 6 20724.71 1986.882 2222.322 17286.379 5209.325
## 7 17853.48 1288.381 14473.393 49781.420 6424.058
## 403.197_0.807 131.0349_0.8 372.0951_0.795 311.0753_0.804 230.0156_0.793
## 2 4133.206 93725.93 1870.997 7916.062 26786.172
## 3 16434.295 52997.45 1706.720 4291.223 20267.777
## 4 5214.395 74554.63 2679.115 1645.019 7272.182
## 5 18957.777 29278.06 1695.265 4510.502 7118.002
## 6 4943.947 62619.96 3602.769 101489.984 194732.380
## 7 3190.854 34408.77 2704.463 5213.963 13361.840
## 181.9916_0.803 675.1192_0.802 248.0596_0.808 203.0021_0.801 230.0135_0.791
## 2 2235.689 2395.5625 4100.937 8536.004 26786.172
## 3 1330.186 502.2117 94115.810 13174.426 173433.340
## 4 1695.219 500.2725 8698.756 9756.973 7272.182
## 5 7599.906 500.0000 12406.228 20716.230 20028.576
## 6 255537.970 1356.0408 8838.046 10468.705 194732.380
## 7 5933.579 1036.9396 40463.098 14179.208 13361.840
## 273.0073_0.807 313.0567_0.793 359.1353_0.806 227.06_0.796 576.1317_0.792
## 2 21640.53 1576.887 1199.965 2327.690 500.0201
## 3 55730.02 2747.077 2705.912 1677.556 502.2117
## 4 32845.93 2117.706 2024.061 2125.570 1130.5149
## 5 41429.94 1615.805 2194.423 2231.195 1044.0050
## 6 56694.30 2740.616 5522.307 18335.857 2239.2495
## 7 43730.25 8706.571 2298.170 1403.551 1486.2402
## 368.0998_0.802 336.0708_0.807 224.0563_0.81 419.1134_0.748 855.1238_0.789
## 2 3468.846 7069.770 40847.93 2662.724 500.0201
## 3 2115.936 5099.174 11843.74 5500.953 502.2117
## 4 1713.510 4117.153 10996.38 2803.256 500.2725
## 5 1230.698 1282.872 11374.53 2390.166 500.0000
## 6 1444.282 4479.453 8285.08 4002.024 1048.2848
## 7 1337.217 3150.002 16682.55 4428.649 502.2908
## 213.9982_0.833 188.9864_0.815 609.1423_0.802 572.1076_0.784 139.0401_0.786
## 2 2720.959 162080.44 500.0201 1015.9120 1527.6703
## 3 4570.818 209628.94 1545.8417 1006.0687 1350.2716
## 4 3981.926 170851.80 500.2725 500.2725 500.2725
## 5 2028.928 255497.62 500.0000 500.0000 1285.3130
## 6 5432.615 87560.83 1138.2489 1088.1221 5499.4810
## 7 12856.666 211470.36 1402.1483 1305.2180 1352.0557
## 325.0323_0.804 181.0505_0.817 227.0559_0.8 124.01_0.775 422.1067_0.783
## 2 1310.002 15064.97 2327.690 2415.2400 1149.372
## 3 1439.722 67733.42 1840.908 1614.5555 1933.433
## 4 1111.012 28290.88 2125.570 1725.7606 1274.432
## 5 1023.366 18807.29 2467.409 1732.1370 1027.562
## 6 2717.388 16499.27 18335.857 2749.5370 1184.949
## 7 28907.125 94906.85 1695.807 502.2908 2038.322
## 313.999_0.818 212.0023_0.825 448.1934_0.807 173.9923_0.799 585.0899_0.796
## 2 5934.950 37405.90 1357.0842 8043.618 500.0201
## 3 14747.123 90601.11 502.2117 16555.283 1217.2870
## 4 6826.158 54552.43 500.2725 16701.996 500.2725
## 5 12779.035 24581.03 500.0000 7784.886 500.0000
## 6 5558.288 99594.68 2584.2312 5053.175 1103.8805
## 7 24121.504 173987.56 2230.3474 17356.809 1146.1119
## 463.107_0.79 477.1007_0.807 347.0076_0.814 167.0211_1.15 335.0494_1.152
## 2 3221.630 1190.4961 1787.5654 403188.2 119424.0
## 3 2015.398 502.2117 1232.8690 314273.6 125768.9
## 4 2161.774 1062.6462 500.2725 334604.5 134767.9
## 5 1300.083 500.0000 1404.9000 373212.3 146170.8
## 6 2268.460 1781.0600 1910.8651 600193.9 270152.7
## 7 2006.003 1363.7043 1203.0910 460556.6 205647.5
## 381.0301_1.155 380.0301_1.155 217.0161_1.159 191.0201_1.159 405.0285_1.16
## 2 35170.88 6337.170 19659.67 363765.7 27292.41
## 3 55100.88 16793.746 41126.77 1733155.1 150123.97
## 4 51315.15 10487.818 42623.04 1347743.6 109597.84
## 5 51177.84 5253.422 38632.85 1152099.2 100218.33
## 6 71307.18 17658.123 33021.25 811499.5 51973.11
## 7 59260.80 33215.348 65129.53 1213337.4 98734.79
## 111.0087_1.164 427.0103_1.161 310.9691_1.162 188.9863_1.166 191.0276_1.165
## 2 34893.67 12452.24 32047.78 65642.76 23857.48
## 3 158133.19 51556.84 65223.33 72952.63 35074.52
## 4 120755.10 42546.73 54402.04 48730.15 29036.61
## 5 109486.63 34395.36 51852.62 109894.02 34110.14
## 6 82226.09 23796.80 41272.41 31848.63 37843.87
## 7 121721.12 37825.11 51626.86 76486.52 34429.62
## 173.0091_1.231 258.044_2.007 375.0931_2.077 227.0554_2.1 324.0723_2.134
## 2 30944.48 2533.6267 500.0201 500.0201 21306.951
## 3 44537.51 502.2117 502.2117 502.2117 9048.322
## 4 34525.39 4374.3315 500.2725 500.2725 9504.115
## 5 28947.87 4493.7190 500.0000 500.0000 2797.101
## 6 29390.31 1068.1229 500.0225 2764.5764 10496.891
## 7 46178.58 1863.5226 502.2908 502.2908 11165.883
## 161.9866_2.133 182.0458_2.174 213.9981_2.254 326.0973_2.32 384.0537_2.335
## 2 500.0201 28041.90 6752.218 1109.6661 2353.773
## 3 159573.4000 34700.82 12230.571 1026.8625 3245.935
## 4 500.2725 19610.77 5277.759 500.2725 2293.590
## 5 7515.8660 15922.64 3254.754 1968.7350 2110.682
## 6 2005.9071 18022.00 7348.520 41790.5160 44484.633
## 7 502.2908 20148.48 20126.840 1599.8134 5405.795
## 653.1834_2.294 675.1659_2.308 348.0769_2.315 446.0335_2.313 326.0892_2.313
## 2 500.0201 500.0201 1950.754 500.0201 1772.146
## 3 502.2117 502.2117 1720.414 502.2117 1728.849
## 4 500.2725 500.2725 1621.403 500.2725 1122.494
## 5 500.0000 500.0000 3782.358 2426.6470 17648.123
## 6 156564.5200 148206.0600 98122.340 67530.6900 843437.060
## 7 502.2908 1043.6969 3945.992 2553.4590 1233.159
## 338.1178_2.307 165.0417_2.342 457.0963_2.367 227.9972_2.377 246.0109_2.385
## 2 500.0201 21899.05 500.0201 65861.16 1096.836
## 3 502.2117 12035.69 502.2117 75080.20 2644.639
## 4 500.2725 24832.46 500.2725 49478.11 1189.202
## 5 500.0000 10827.28 500.0000 17093.96 2750.470
## 6 500.0225 14773.14 2259.8994 27637.51 66962.586
## 7 502.2908 38866.59 502.2908 32202.36 2549.245
## 206.9967_2.418 479.0459_2.443 233.0124_2.47 645.1901_2.522 309.0242_2.522
## 2 15948.39 500.0201 33029.730 500.0201 500.0201
## 3 34906.75 502.2117 28601.252 502.2117 1160.0740
## 4 16647.91 500.2725 12021.533 500.2725 1167.9474
## 5 29180.84 500.0000 10164.411 500.0000 1164.2970
## 6 26154.96 500.0225 5366.024 500.0225 1743.6094
## 7 28900.77 502.2908 24986.861 502.2908 2152.3250
## 230.0129_2.513 230.0159_2.501 483.0154_2.51 232.0047_2.518 516.1022_2.528
## 2 18111.777 16665.170 1284.0045 1882.2661 1137.5221
## 3 18752.578 17105.092 502.2117 2045.0801 1284.2080
## 4 2599.209 2599.209 500.2725 500.2725 500.2725
## 5 31888.178 24080.053 500.0000 2336.1060 1134.3900
## 6 791158.000 38006.598 47916.9340 39621.2150 12032.2110
## 7 3692.041 2773.920 1381.8585 1461.1941 1284.2013
## 359.0975_2.524 274.0027_2.51 565.137_2.579 218.1033_2.56 489.0936_2.593
## 2 1599.7126 5232.607 500.0201 9406.848 500.0201
## 3 1188.7185 47321.297 502.2117 14377.957 1512.1270
## 4 500.2725 4110.276 500.2725 10466.689 500.2725
## 5 1166.5920 13986.703 500.0000 8230.018 500.0000
## 6 3931.9773 4022.951 500.0225 10757.546 500.0225
## 7 1577.5042 22292.084 502.2908 8732.938 1315.8228
## 422.0544_2.559 432.0832_2.56 579.0437_2.578 223.0583_2.562 611.0554_2.581
## 2 1104.2598 500.0201 1039.5365 500.0201 500.0201
## 3 502.2117 502.2117 502.2117 502.2117 502.2117
## 4 500.2725 500.2725 1002.3151 500.2725 1092.6956
## 5 500.0000 500.0000 500.0000 500.0000 500.0000
## 6 500.0225 500.0225 500.0225 500.0225 500.0225
## 7 502.2908 502.2908 1737.7618 502.2908 502.2908
## 609.0591_2.577 449.0698_2.589 650.1898_2.57 364.0955_2.559 320.1087_2.579
## 2 500.0201 1219.1797 500.0201 500.0201 500.0201
## 3 502.2117 1021.5049 502.2117 502.2117 502.2117
## 4 500.2725 500.2725 500.2725 500.2725 500.2725
## 5 500.0000 500.0000 500.0000 500.0000 500.0000
## 6 1051.0883 1125.1115 500.0225 500.0225 500.0225
## 7 502.2908 1406.8080 502.2908 502.2908 502.2908
## 494.0742_2.602 579.126_2.604 361.0218_2.595 295.03_2.565 293.0743_2.543
## 2 500.0201 500.0201 1896.596 500.0201 1251.028
## 3 502.2117 1187.2145 1686.038 1244.3517 1513.065
## 4 500.2725 500.2725 1616.001 500.2725 1981.003
## 5 500.0000 500.0000 1625.198 1469.2060 1259.049
## 6 500.0225 1529.4370 6475.295 4355.3706 2277.065
## 7 502.2908 1628.0400 1148.918 1191.4563 1544.767
## 293.0331_2.576 484.0498_2.566 623.0319_2.6 204.9812_2.586 307.0111_2.574
## 2 1513.241 1068.2694 500.0201 50909.45 500.0201
## 3 2188.218 1088.0094 1464.6678 26349.61 502.2117
## 4 1896.873 500.2725 1028.0994 76979.25 1260.3247
## 5 3129.836 500.0000 500.0000 41582.26 1192.4130
## 6 50455.020 1007.6409 1067.2473 126316.24 8508.9480
## 7 1738.154 1098.5785 1018.5519 104325.95 502.2908
## 328.9944_2.584 356.1002_2.592 168.0302_2.597 801.2049_2.656 389.1059_2.672
## 2 500.0201 1126.532 17155.63 500.0201 500.0201
## 3 1513.3253 1004.830 22743.08 502.2117 1421.5360
## 4 1029.7555 1128.579 24585.02 500.2725 500.2725
## 5 500.0000 500.000 26314.13 500.0000 500.0000
## 6 2172.9480 38366.895 19339.69 500.0225 10951.8910
## 7 1161.0303 2596.624 20257.09 1234.3824 1916.8765
## 487.0759_2.632 675.1222_2.643 457.097_2.652 675.2009_2.65 509.0519_2.681
## 2 500.0201 500.0201 500.0201 500.0201 2563.280
## 3 502.2117 1012.2609 502.2117 502.2117 3832.673
## 4 500.2725 500.2725 500.2725 500.2725 2756.965
## 5 500.0000 500.0000 500.0000 500.0000 5336.205
## 6 1562.7051 500.0225 2590.1562 1027.5782 1662.388
## 7 502.2908 1045.9308 1177.7500 502.2908 2306.194
## 590.1471_2.657 178.0524_2.721 216.9812_2.65 829.2715_2.727 323.0414_2.68
## 2 500.0201 430067.5 29082.23 3268.4067 1270.5984
## 3 502.2117 558012.4 43089.94 5805.1704 502.2117
## 4 500.2725 302084.4 20813.85 1082.7236 500.2725
## 5 500.0000 445030.1 24046.23 500.0000 500.0000
## 6 500.0225 171996.0 48939.37 500.0225 5156.4673
## 7 1154.0962 386786.5 50271.47 12940.9280 1222.4187
## 835.2888_2.725 303.072_2.684 260.0239_2.686 659.1667_2.763 669.1448_2.74
## 2 4796.181 12730.77 1589.989 5241.6860 8758.166
## 3 7558.742 17661.34 1312.136 7732.2590 16029.628
## 4 1253.800 12084.00 1296.753 1978.4995 4091.143
## 5 500.000 21257.04 3538.354 3215.1780 2959.554
## 6 1105.322 25477.35 54481.520 500.0225 3764.209
## 7 15031.091 29635.99 1088.153 5833.2200 22590.621
## 313.9951_2.734 596.1248_2.733 480.1382_2.735 194.0459_2.735 194.0495_2.735
## 2 5580.389 1975.0679 8888.596 47558.73 47558.727
## 3 10188.704 2632.9190 19508.867 119709.36 119709.360
## 4 9679.087 1078.3878 8442.520 90103.25 7482.223
## 5 11386.321 2541.9590 7655.292 108583.84 9327.712
## 6 6570.588 500.0225 7068.976 63520.39 63520.387
## 7 21870.379 2943.0490 97898.510 392848.94 27427.893
## 411.0814_2.735 395.0857_2.735 379.0909_2.789 549.1963_2.753 744.2193_2.763
## 2 1161.512 7264.742 39631.17 39381.41 4912.695
## 3 4568.208 16883.516 44951.27 61347.68 7099.320
## 4 2500.219 11030.845 22708.02 13445.17 1960.904
## 5 3053.327 18969.777 44086.99 10282.17 1940.490
## 6 2178.001 4956.781 9066.33 12858.31 1098.712
## 7 25949.787 32805.008 26848.96 124875.27 11788.747
## 263.1041_2.759 464.1437_2.772 264.1071_2.755 665.1832_2.777 178.0515_2.811
## 2 569173.9 53509.53 97138.41 7740.333 582890.1
## 3 825874.3 77648.02 138249.42 8456.492 629197.4
## 4 291803.0 22589.67 48649.60 2622.571 330607.8
## 5 256603.9 29994.00 45349.05 4272.781 543115.2
## 6 292615.9 16214.35 50580.37 1148.283 216391.0
## 7 1521374.0 77026.53 218575.60 6747.918 548982.2
## 188.9864_2.765 829.2715_2.758 835.2887_2.754 195.0514_2.765 396.0904_2.751
## 2 125332.46 3268.407 4796.181 9668.152 2773.470
## 3 171900.19 5805.170 7558.742 16462.285 4553.333
## 4 126318.16 1082.724 1253.800 44346.164 3827.334
## 5 221183.20 500.000 500.000 12144.354 4661.544
## 6 75570.37 1149.604 1260.793 42334.406 2355.992
## 7 182080.31 12940.928 15966.051 129361.840 9247.482
## 459.1629_2.778 391.1122_2.774 263.1397_2.777 383.0644_2.771 242.0128_2.785
## 2 1440.468 500.0201 24785.248 10508.136 87696.150
## 3 2860.357 502.2117 36097.610 10048.537 39388.586
## 4 3474.981 3514.7710 11254.750 6677.061 4049.327
## 5 500.000 500.0000 9767.227 6365.724 3620.569
## 6 3436.175 2802.4949 11711.818 9871.016 4309.526
## 7 28042.479 10796.5210 64490.080 24387.041 5961.074
## 263.0272_2.81 931.1224_2.809 669.1447_2.763 551.0331_2.81 835.0489_2.8
## 2 18140.54 1254.2596 8758.166 1184.872 500.0201
## 3 15501.46 1108.3787 15741.449 1241.011 1436.2246
## 4 12893.21 500.2725 4091.143 1162.429 500.2725
## 5 17381.93 1192.1540 2959.554 1748.406 500.0000
## 6 108861.81 500.0225 3764.209 1210.977 1288.9585
## 7 12464.62 1427.3160 22590.621 1598.597 1269.8384
## 246.9917_2.802 565.0065_2.811 263.0591_2.805 331.091_2.747 549.1196_2.793
## 2 27239.266 1493.6395 1163.3062 56195.74 1929.513
## 3 98978.900 502.2117 1078.2225 57986.16 1893.382
## 4 19829.844 1257.8796 500.2725 38724.47 1031.834
## 5 33894.020 1155.7850 1244.9700 32944.37 1270.531
## 6 7881.444 1449.9929 3014.2866 44828.70 3602.184
## 7 20859.158 1317.6528 1540.5650 73977.19 2059.146
## 265.0184_2.828 468.0834_2.815 464.0663_2.822 659.1239_2.825 549.035_2.814
## 2 1264.222 1004.6373 1094.840 500.0201 1317.6860
## 3 1211.664 502.2117 1611.516 502.2117 1155.6483
## 4 1023.207 500.2725 1514.511 500.2725 500.2725
## 5 1147.350 1006.6750 1764.189 500.0000 500.0000
## 6 6242.403 7345.4976 3142.148 500.0225 1219.5210
## 7 1282.289 1068.5092 1294.544 502.2908 1213.9509
## 645.1099_2.832 383.0324_2.787 473.0218_2.809 181.9917_2.823 549.1963_2.777
## 2 1395.7289 5912.826 3270.011 1184.965 39381.41
## 3 1463.8264 4728.077 3020.374 1102.500 61347.68
## 4 500.2725 4706.573 2659.091 1683.864 13445.17
## 5 1050.5460 3316.681 2979.912 4367.650 10282.17
## 6 500.0225 8058.697 4164.370 137960.950 13319.27
## 7 1493.8925 5256.630 2292.661 3359.276 124875.27
## 464.1437_2.808 419.1155_2.861 610.164_2.859 896.2543_2.86 219.0524_2.85
## 2 60517.58 500.0201 5761.977 1530.5090 500.0201
## 3 77648.02 502.2117 6249.296 1049.4247 502.2117
## 4 22589.67 500.2725 2146.551 500.2725 500.2725
## 5 29994.00 500.0000 500.000 500.0000 13221.7040
## 6 18029.01 5512.6924 2944.638 500.0225 500.0225
## 7 80643.57 502.2908 21186.635 4827.6700 16056.6080
## 505.1427_2.841 580.1297_2.848 324.0723_2.857 665.1833_2.829 219.0509_2.854
## 2 1196.0145 5893.484 30983.62 8325.764 500.0201
## 3 1200.0968 4994.884 27085.79 8456.492 502.2117
## 4 500.2725 2140.148 25058.81 2833.050 1030.7941
## 5 2385.1800 4268.077 11818.71 4272.781 15151.2940
## 6 1112.1592 1546.441 31264.04 1989.700 1124.0906
## 7 38062.6170 2892.536 98016.70 6747.918 232285.7000
## 659.1666_2.837 263.1041_2.796 645.1893_2.881 560.1364_2.889 441.0612_2.876
## 2 6892.925 552014.1 1417.7347 500.0201 11581.268
## 3 7546.528 790106.5 1507.5568 1010.2306 9240.389
## 4 1986.536 291803.0 500.2725 500.2725 5040.635
## 5 3215.178 256603.9 500.0000 500.0000 9564.785
## 6 1191.755 295047.3 1813.0662 1330.9205 4080.586
## 7 6881.403 1381773.8 1361.5044 1112.5435 5153.303
## 574.1142_2.883 343.0669_2.881 178.0513_2.879 379.0909_2.88 241.1193_2.896
## 2 3906.262 25783.293 712494.6 56203.88 19704.229
## 3 4457.307 11092.471 623981.8 42185.87 43030.293
## 4 1635.880 12100.050 352942.9 21750.94 36307.110
## 5 3542.160 3660.505 543115.2 44153.90 9643.351
## 6 1020.167 2780.454 253609.4 12113.66 17985.604
## 7 2298.904 18084.572 580858.1 32058.97 23183.832
## 203.0021_2.888 246.0383_2.873 574.1523_2.913 134.0611_2.89 370.0781_2.895
## 2 16123.00 72523.94 500.0201 27422.166 1753.095
## 3 48660.64 60718.72 502.2117 23332.582 1532.476
## 4 18991.71 55614.60 500.2725 12033.973 2974.486
## 5 28931.72 83623.16 500.0000 20631.459 1742.336
## 6 16632.70 42570.63 1073.2687 8854.524 2740.117
## 7 19111.61 41042.73 1010.2699 20185.771 15739.560
## 468.0438_2.896 277.0007_2.942 464.1436_2.856 432.1639_2.932 395.0857_2.92
## 2 1630.944 2764.456 60517.58 500.0201 4678.250
## 3 1174.221 1816.158 62852.19 502.2117 19229.000
## 4 1794.558 3747.042 17987.10 500.2725 4045.293
## 5 1024.719 2219.419 29994.00 500.0000 8879.860
## 6 1138.251 5460.667 18029.01 2839.2302 2433.522
## 7 2265.843 4001.672 80643.57 502.2908 13934.565
## 194.0459_2.921 373.1067_2.937 659.2045_2.918 389.1005_2.94 388.047_2.937
## 2 24421.23 5205.502 500.0201 500.0201 21387.04
## 3 135220.53 28267.598 1275.6581 7641.4185 36570.96
## 4 28190.68 3377.223 500.2725 1311.1658 11045.64
## 5 42752.77 9139.062 500.0000 1345.6640 11872.90
## 6 39412.07 11700.846 1064.9032 9700.9730 11901.34
## 7 146909.39 29116.938 1136.2034 11689.8130 25565.73
## 422.1084_2.915 473.0996_2.932 428.0941_2.98 157.0506_2.959 357.0826_2.931
## 2 500.0201 15568.201 500.0201 23276.57 4140.472
## 3 502.2117 38337.500 1212.6360 23729.44 1923.603
## 4 500.2725 6745.863 500.2725 40769.50 3403.037
## 5 500.0000 4761.560 500.0000 14662.70 2746.002
## 6 1230.8770 9643.778 3624.5490 38093.66 3540.575
## 7 1393.5662 37457.280 1315.9333 26698.38 20611.307
## 459.0843_2.964 263.1054_2.91 623.1618_2.946 374.0316_2.965 227.0628_2.987
## 2 7907.939 522072.0 1284.4814 11341.154 1012.7760
## 3 16352.291 583875.6 1028.4437 18363.053 502.2117
## 4 6574.000 226123.2 500.2725 12535.910 500.2725
## 5 3871.335 204995.9 500.0000 11371.824 500.0000
## 6 2946.049 295047.3 1066.5320 3823.347 10034.8740
## 7 16488.660 1323426.1 1008.9670 16596.332 502.2908
## 477.1039_2.949 139.0401_3.003 227.0551_2.986 609.1484_2.937 322.1104_2.943
## 2 500.0201 1051.8927 1485.8943 2539.2603 500.0201
## 3 1285.8231 1024.3806 1217.1273 1957.0605 502.2117
## 4 500.2725 500.2725 1569.8837 500.2725 1002.4456
## 5 500.0000 500.0000 1152.8790 1008.6580 500.0000
## 6 1268.8782 4151.3193 14865.2890 500.0225 500.0225
## 7 1123.1480 1233.3864 502.2908 3621.3490 502.2908
## 1093.9937_2.988 785.2097_2.974 336.1218_3.046 563.1044_2.949 487.0726_2.982
## 2 14059.90 500.0201 1228.127 1366.604 1291.2161
## 3 19792.44 502.2117 1322.599 1505.870 502.2117
## 4 24328.44 500.2725 1852.088 1219.286 1037.9515
## 5 21201.11 500.0000 1044.224 1060.485 1200.0750
## 6 10119.53 500.0225 1324.446 1141.150 1299.7789
## 7 19701.69 1508.3335 1304.316 2479.142 1657.1627
## 389.1043_2.952 173.0001_2.997 577.1184_2.969 771.1944_2.999 368.9723_3.004
## 2 500.0201 15839.01 500.0201 500.0201 5975.873
## 3 3734.0420 27973.33 1818.1772 502.2117 26272.984
## 4 1241.3461 27233.02 500.2725 500.2725 23327.082
## 5 500.0000 18762.62 500.0000 500.0000 12524.897
## 6 9700.9730 10526.39 500.0225 500.0225 4273.046
## 7 6027.4710 27079.77 1620.6034 502.2908 18753.400
## 172.9915_3.001 359.1353_3.002 382.9875_3.002 875.1494_2.993 555.0787_2.996
## 2 228495.2 2235.382 12367.22 500.0201 500.0201
## 3 543342.4 2762.093 58994.66 502.2117 502.2117
## 4 461902.0 3007.415 22580.51 500.2725 500.2725
## 5 275754.2 2034.514 12267.22 500.0000 1000.2270
## 6 149352.1 4497.645 11250.29 500.0225 1806.5128
## 7 432861.4 15491.334 37467.49 1036.0077 1070.4640
## 277.0329_2.993 855.1197_2.99 645.1887_2.968 360.1388_3.006 224.0561_3.018
## 2 1357.026 500.0201 500.0201 1218.9586 30314.693
## 3 1085.498 502.2117 502.2117 1373.2695 7335.350
## 4 2463.606 500.2725 500.2725 500.2725 8555.215
## 5 1895.586 500.0000 500.0000 500.0000 8715.640
## 6 6097.403 500.0225 2040.2936 1191.8896 8576.362
## 7 3241.910 502.2908 502.2908 2906.1467 16478.840
## 1117.2561_3.018 861.1356_3.001 560.1365_2.971 800.2743_3.027 181.0504_3.017
## 2 500.0201 500.0201 500.0201 500.0201 24167.77
## 3 502.2117 502.2117 502.2117 502.2117 123787.59
## 4 500.2725 500.2725 500.2725 1187.7285 38108.65
## 5 500.0000 500.0000 500.0000 500.0000 29161.83
## 6 500.0225 500.0225 2716.6870 500.0225 27173.99
## 7 502.2908 502.2908 502.2908 502.2908 176814.88
## 741.1857_3.018 359.0958_2.977 1123.2728_3.021 457.0623_2.989 741.1856_3.019
## 2 500.0201 1950.8844 500.0201 1511.4958 500.0201
## 3 502.2117 502.2117 502.2117 2918.6355 502.2117
## 4 500.2725 1433.5070 500.2725 500.2725 500.2725
## 5 500.0000 1120.7130 500.0000 1434.8150 500.0000
## 6 1464.4463 99050.0550 500.0225 8062.7080 1464.4463
## 7 502.2908 2729.3610 502.2908 2425.1157 502.2908
## 479.0465_3.005 547.1102_3.005 418.1842_3.015 569.0928_2.988 427.0844_2.991
## 2 1015.8890 1115.2130 500.0201 500.0201 500.0201
## 3 1293.2338 1254.8406 1044.8848 1342.1074 1358.4607
## 4 500.2725 500.2725 500.2725 500.2725 500.2725
## 5 500.0000 1132.7670 1129.5000 1024.9190 1216.8820
## 6 9337.2300 1172.8724 18896.3100 4701.3647 15889.6640
## 7 1005.1578 502.2908 502.2908 1022.4750 1556.0026
## 951.181_3.008 364.0972_3.027 755.2012_3.03 189.0027_3.097 1131.2716_3.026
## 2 500.0201 500.0201 500.0201 34493.90 500.0201
## 3 502.2117 502.2117 502.2117 77083.49 502.2117
## 4 500.2725 500.2725 500.2725 26709.77 500.2725
## 5 500.0000 500.0000 500.0000 19874.87 500.0000
## 6 500.0225 500.0225 500.0225 33752.50 500.0225
## 7 502.2908 502.2908 502.2908 58609.12 502.2908
## 945.1645_3.008 605.1126_3.023 379.0338_3.038 1137.288_3.026 230.0128_3.042
## 2 500.0201 3806.9210 5589.884 500.0201 42695.720
## 3 502.2117 2101.3250 12932.403 502.2117 164720.300
## 4 500.2725 500.2725 5847.483 500.2725 9055.093
## 5 500.0000 500.0000 5296.986 500.0000 25247.043
## 6 500.0225 500.0225 3140.232 500.0225 6230.308
## 7 502.2908 1248.9188 7005.935 502.2908 9821.015
## 814.2904_3.028 336.1214_3.081 432.2027_3.06 828.3048_3.061 500.1902_3.066
## 2 500.0201 1702.684 1075.9193 500.0201 500.0201
## 3 502.2117 1711.119 502.2117 502.2117 502.2117
## 4 500.2725 1852.088 500.2725 500.2725 500.2725
## 5 500.0000 1044.224 500.0000 500.0000 500.0000
## 6 500.0225 1362.488 21346.4320 500.0225 6366.7334
## 7 502.2908 1304.316 502.2908 502.2908 502.2908
## 361.1499_3.066 109.0294_3.11 188.0103_3.118 330.0287_3.095 337.056_3.104
## 2 1650.420 500.0201 64036.36 12306.473 43507.094
## 3 7590.456 1165.1726 144177.08 5873.322 1467.145
## 4 3670.923 1267.8936 44057.82 4786.049 1786.522
## 5 2687.157 500.0000 29924.88 18903.006 3224.266
## 6 8064.451 1546.4446 62198.76 13038.831 1684.150
## 7 12763.809 502.2908 104678.06 5482.946 9325.253
## 197.0442_3.134 375.1278_3.109 979.2124_3.136 1108.0095_3.117 397.0032_3.133
## 2 1582.025 2554.633 500.0201 39322.97 39249.46
## 3 1201.067 9047.228 502.2117 46984.04 135508.28
## 4 2969.080 5938.353 500.2725 33850.31 21442.41
## 5 1757.130 1790.583 500.0000 35272.83 13288.42
## 6 2792.003 6773.089 500.0225 37220.92 38784.61
## 7 1808.867 21871.062 502.2908 49979.11 84667.98
## 583.1092_3.147 889.1651_3.147 187.0383_3.122 187.0075_3.121 973.198_3.133
## 2 1121.1277 500.0201 28279.00 725395.7 500.0201
## 3 1553.4073 502.2117 68913.64 1835242.2 502.2117
## 4 500.2725 500.2725 16783.30 453787.2 500.2725
## 5 500.0000 500.0000 11901.39 327898.0 500.0000
## 6 3172.1243 500.0225 25334.32 657575.0 500.0225
## 7 1627.6866 502.2908 49611.68 1310167.2 502.2908
## 313.0569_3.13 189.0026_3.12 373.0764_3.153 883.1498_3.145 209.0452_3.159
## 2 500.0201 40609.09 1451.3241 500.0201 500.0201
## 3 502.2117 77083.49 502.2117 502.2117 502.2117
## 4 1100.7655 26800.71 500.2725 500.2725 500.2725
## 5 500.0000 19874.87 500.0000 500.0000 500.0000
## 6 3479.0872 36243.54 1334.4143 500.0225 1454.0785
## 7 3640.0960 58609.12 1544.6494 502.2908 1420.8890
## 254.9941_3.14 211.0611_3.158 747.2342_3.15 413.1436_3.2 493.0653_3.147
## 2 30327.89 3144.810 500.0201 1291.383 1505.315
## 3 49599.29 8091.964 502.2117 1676.560 1463.216
## 4 22590.53 3917.637 500.2725 1659.196 1354.618
## 5 17440.75 4739.452 500.0000 1180.482 1028.109
## 6 29116.41 5361.171 500.0225 1563.184 3342.816
## 7 38267.12 3043.821 502.2908 1831.222 1593.804
## 276.0009_3.151 769.2171_3.155 373.1513_3.174 471.0817_3.145 397.0032_3.134
## 2 500.0201 500.0201 1431.4451 1211.033 39249.46
## 3 502.2117 502.2117 502.2117 1474.248 135508.28
## 4 500.2725 500.2725 1178.6276 1141.402 21442.41
## 5 11798.1370 500.0000 1601.2520 1174.738 13288.42
## 6 45853.5740 500.0225 1393.0183 1658.320 38784.61
## 7 502.2908 502.2908 1581.8109 1496.678 84667.98
## 218.9964_3.175 373.114_3.2 175.0611_3.177 230.9968_3.178 429.0831_3.189
## 2 6321.511 2925.521 10914.024 13176.93 28858.9450
## 3 24121.707 5051.859 9954.097 25346.69 18626.2900
## 4 27496.152 4485.041 18593.074 13596.57 1156.7950
## 5 25566.215 2577.343 26890.793 61188.60 500.0000
## 6 5877.723 27763.686 5454.308 23754.15 500.0225
## 7 17027.771 10832.516 7648.310 38262.94 502.2908
## 311.0752_3.182 785.1841_3.18 441.1026_3.212 1124.0041_3.197 599.1056_3.199
## 2 1799.559 500.0201 1254.1736 1171.282 500.0201
## 3 2623.834 502.2117 502.2117 1529.921 502.2117
## 4 1156.815 500.2725 500.2725 2603.463 500.2725
## 5 2230.256 500.0000 500.0000 3558.246 500.0000
## 6 59223.190 500.0225 5217.1530 1624.317 1091.4772
## 7 5574.253 502.2908 502.2908 1314.718 502.2908
## 493.0642_3.179 428.9933_3.184 203.0048_3.202 412.9977_3.204 204.9979_3.197
## 2 1495.074 1401.918 27376.50 4577.090 1948.891
## 3 1393.233 4389.131 43821.94 11395.879 3473.205
## 4 1296.818 2192.729 44778.07 4682.350 3641.913
## 5 1029.883 1831.237 70541.99 6593.104 5097.104
## 6 3342.816 1069.197 41308.26 8312.104 3152.634
## 7 1086.062 2056.286 34643.69 8231.425 3754.874
## 203.002_3.205 303.0178_3.212 370.097_3.208 448.1959_3.232 1108.0095_3.194
## 2 27376.50 4841.248 7298.047 500.0201 39322.97
## 3 43821.94 8432.901 11697.973 502.2117 40273.17
## 4 44778.07 5308.708 4489.029 500.2725 30145.45
## 5 70541.99 6000.432 3371.135 500.0000 32093.63
## 6 41308.26 29464.395 5011.553 4333.9290 34589.28
## 7 34643.69 16405.049 15327.748 502.2908 42463.14
## 429.083_3.201 593.1477_3.228 403.1971_3.26 605.1462_3.238 261.0071_3.241
## 2 28858.9450 500.0201 4809.041 500.0201 1312.109
## 3 18626.2900 502.2117 5487.453 502.2117 6019.498
## 4 1156.7950 500.2725 1935.737 500.2725 2738.302
## 5 500.0000 500.0000 13184.255 500.0000 9583.148
## 6 500.0225 1024.6782 3571.718 500.0225 2233.929
## 7 502.2908 502.2908 2509.020 1458.7856 28961.799
## 209.0452_3.24 368.0971_3.297 509.0409_3.291 1004.2068_3.286 675.9686_3.302
## 2 500.0201 1528.057 16441.803 500.0201 1067.351
## 3 502.2117 1605.141 23053.166 502.2117 6176.889
## 4 500.2725 1167.445 1587.675 500.2725 1225.190
## 5 500.0000 500.000 500.000 500.0000 1599.655
## 6 1335.0479 1091.769 1391.278 500.0225 1230.140
## 7 1420.8890 1252.586 1236.995 502.2908 10873.561
## 608.1059_3.3 423.9954_3.299 681.985_3.302 212.0348_3.301 212.0109_3.298
## 2 500.0201 4184.568 1686.953 19439.395 29946.07
## 3 1257.9000 14292.380 10467.930 53517.110 35281.39
## 4 500.2725 3379.856 2960.251 25333.893 35919.44
## 5 500.0000 1659.044 500.000 9232.063 21052.74
## 6 2132.1924 5947.771 2097.885 20752.488 35933.54
## 7 1638.5148 12934.177 26128.184 84059.914 2170537.00
## 1133.0047_3.302 258.9926_3.301 421.9984_3.301 448.9914_3.302 279.9896_3.301
## 2 12797.53 4464.992 35450.49 4404.797 35932.04
## 3 14018.19 20303.938 123168.12 12268.920 51047.71
## 4 23355.42 2779.143 27037.97 4722.067 43687.11
## 5 13411.94 16626.994 10325.75 1671.214 27310.98
## 6 14721.55 10513.363 45261.73 4655.233 44516.51
## 7 25735.38 11031.125 118986.44 21651.217 68140.02
## 459.0926_3.306 336.0718_3.292 213.9981_3.303 212.0027_3.304 446.9937_3.304
## 2 17902.4410 16148.072 27911.19 515011.8 28600.30
## 3 14738.7490 9804.806 63811.21 1357483.8 113461.44
## 4 1076.4628 7657.444 30817.93 618903.8 42696.15
## 5 500.0000 2649.646 16270.61 271142.4 10988.39
## 6 500.0225 11947.929 37721.73 624528.3 39930.95
## 7 1484.4175 4903.376 90533.00 2170537.0 204963.83
## 572.1094_3.326 429.0423_3.352 215.0021_3.307 594.0962_3.363 283.0822_3.328
## 2 500.0201 2229.628 30515.834 4259.085 40946.71
## 3 502.2117 1990.655 44670.700 2377.000 59168.20
## 4 1203.5193 1930.752 8639.171 1370.145 34351.00
## 5 500.0000 1217.086 92194.445 1024.509 14026.34
## 6 500.0225 2240.363 31006.004 1611.053 47110.36
## 7 502.2908 3608.470 17411.102 4252.189 85490.80
## 569.0956_3.38 245.0124_3.342 755.1981_3.389 273.0074_3.349 347.0881_3.328
## 2 1287.2800 8036.699 500.0201 21716.80 500.0201
## 3 1274.6588 56800.040 502.2117 58623.13 502.2117
## 4 500.2725 15923.605 500.2725 32809.43 1064.0841
## 5 1122.0020 15662.349 500.0000 43919.93 500.0000
## 6 1236.8182 9428.432 500.0225 47351.66 1424.5940
## 7 1189.5416 65532.492 1470.4814 44389.17 2911.9338
## 243.1349_3.353 237.0356_3.369 574.1534_3.396 457.0651_3.354 404.0395_3.379
## 2 37066.195 500.0201 500.0201 500.0201 1617.215
## 3 15518.830 502.2117 502.2117 502.2117 1894.123
## 4 28669.371 500.2725 1050.2803 1061.5360 1597.454
## 5 5885.612 1284.5580 500.0000 500.0000 1430.279
## 6 21825.283 1065.7505 1038.6943 1686.2825 1847.706
## 7 9667.562 1024.9375 1054.7756 1319.7858 2474.001
## 350.088_3.366 388.047_3.398 308.0774_3.378 345.1554_3.382 357.0825_3.399
## 2 28473.035 23033.50 8928.033 3215.546 1369.4939
## 3 34917.164 37494.97 2757.698 14827.999 1168.5048
## 4 33827.992 13517.07 3893.159 7265.261 1548.8846
## 5 7937.236 13641.19 1195.560 3706.526 2546.2840
## 6 19562.957 11891.32 4109.624 12293.021 500.0225
## 7 16392.744 23257.72 22572.178 20402.740 22256.2710
## 283.0822_3.343 381.0802_3.385 347.17_3.394 645.1896_3.451 372.1099_3.404
## 2 40946.71 1248.590 1777.507 2839.900 4393.820
## 3 59168.20 1297.402 1619.688 3352.042 2316.000
## 4 34351.00 1594.837 8759.633 1625.784 22392.506
## 5 14026.34 1237.889 1235.619 1933.333 9403.422
## 6 47110.36 2476.594 16943.121 1261.059 1336.347
## 7 85490.80 1424.450 7108.137 2030.491 1796.516
## 380.0942_3.483 359.1347_3.481 178.0557_3.444 240.9816_3.419 393.0614_3.427
## 2 32171.969 6637.607 33387.32 76310.164 18147.82
## 3 23183.588 10761.731 35296.32 2467.596 18278.29
## 4 22360.615 9616.301 42495.64 6403.571 20145.70
## 5 32012.945 7535.427 1492137.80 27549.479 17839.29
## 6 8855.116 10072.250 30864.30 4978.031 27679.47
## 7 16139.827 31457.898 37236.37 13784.179 23142.68
## 659.2057_3.452 275.0224_3.427 331.091_3.433 297.0036_3.423 336.0706_3.376
## 2 1011.924 1420.384 60085.22 1354.675 2559.7950
## 3 1295.666 7311.334 59490.22 1590.617 1734.8120
## 4 1025.241 1792.416 64656.66 1537.640 1323.7981
## 5 500.000 2959.763 46114.14 1095.457 500.0000
## 6 1459.686 2168.902 90255.98 1227.812 500.0225
## 7 1565.184 1959.251 85001.95 1731.376 1036.4287
## 361.1502_3.49 569.1751_3.441 338.0632_3.433 246.0383_3.487 321.0622_3.448
## 2 2958.115 6952.455 1654.4623 92775.07 29864.20
## 3 11504.693 11497.287 502.2117 75916.83 30422.84
## 4 5831.146 3894.294 500.2725 97662.73 31541.70
## 5 3116.306 1575.881 500.0000 113493.88 25448.34
## 6 5918.629 6497.292 500.0225 72250.68 30603.79
## 7 8466.972 18514.070 502.2908 59849.73 31963.03
## 636.1806_3.455 264.1168_3.46 611.1667_3.462 526.114_3.463 675.1598_3.47
## 2 6597.000 18984.12 11994.771 25346.26 1367.005
## 3 6171.389 209550.14 13618.314 22599.65 1885.297
## 4 3551.707 12719.38 8018.057 18018.14 1052.258
## 5 1692.240 11339.95 5001.151 17458.89 2040.139
## 6 3185.529 15336.21 11976.289 13655.16 1696.083
## 7 5005.007 34772.86 18729.188 18866.61 1665.976
## 759.1899_3.464 663.1291_3.465 473.0995_3.464 1115.3646_3.466 835.2891_3.466
## 2 6770.371 17872.627 26638.65 9007.860 30294.428
## 3 17925.996 25745.963 71092.49 14915.790 54536.950
## 4 3023.302 8960.390 11244.18 1858.087 9193.083
## 5 2437.424 5286.119 9080.14 500.000 3969.529
## 6 3502.183 10144.628 23421.84 2555.373 12742.841
## 7 17017.307 31538.523 57073.75 40242.094 93430.370
## 263.141_3.465 669.1453_3.465 565.1701_3.465 1121.3811_3.466 813.2953_3.466
## 2 91165.72 31356.99 8595.822 6712.300 6448.335
## 3 104570.13 34249.31 12769.933 9404.188 11707.745
## 4 26066.00 13491.70 3599.476 1444.898 2950.242
## 5 32622.40 10226.50 2471.274 500.000 1094.941
## 6 47276.62 18428.29 5163.568 1624.077 2361.416
## 7 129550.09 51002.49 19104.190 21716.146 24760.592
## 361.0713_3.465 829.2722_3.466 549.1965_3.467 971.2036_3.465 263.1046_3.466
## 2 27392.88 24744.951 202512.72 9367.938 2398278.2
## 3 32319.60 38729.402 262769.66 12603.446 3079720.0
## 4 14878.26 5787.452 80314.77 4049.789 984361.2
## 5 11316.18 5231.621 48212.84 2011.288 766227.8
## 6 15289.39 7975.654 92371.72 4322.829 1079254.1
## 7 38964.18 74986.220 437470.50 16567.967 4669233.0
## 383.053_3.466 527.2139_3.467 728.2422_3.466 949.2215_3.466 145.0619_3.467
## 2 86987.21 20327.654 8234.713 7274.354 64166.74
## 3 101857.53 35878.977 11448.226 12761.218 78737.68
## 4 64309.71 6010.072 4072.095 3739.259 33152.98
## 5 55675.14 4094.643 3181.435 1704.055 25409.91
## 6 69386.62 8192.310 2556.099 2641.255 33648.38
## 7 119541.63 57606.130 11523.113 22226.434 127784.88
## 744.2195_3.468 1030.3123_3.466 750.2363_3.468 480.1372_3.468 1257.2949_3.465
## 2 27855.553 12820.266 29474.230 1708.626 4634.305
## 3 34246.875 14998.200 30712.836 2180.960 4342.844
## 4 12259.015 3155.575 12023.797 2122.272 1329.293
## 5 10039.240 1941.837 10111.634 1813.109 500.000
## 6 7884.743 2729.789 9491.534 2119.340 1443.247
## 7 34506.367 16687.030 26660.223 3721.552 9309.012
## 674.1395_3.477 643.1894_3.473 230.9969_3.457 945.2595_3.469 464.1438_3.474
## 2 4456.714 8002.753 2387.211 14134.877 191703.73
## 3 7142.246 8498.534 5497.538 14474.374 181057.97
## 4 3665.454 4157.405 7491.002 5632.112 116001.09
## 5 2976.649 4629.730 37576.363 5843.373 117608.93
## 6 3044.648 2571.298 5612.942 2455.502 77506.01
## 7 5177.909 6552.012 16211.397 12756.392 162613.12
## 659.1667_3.474 665.1835_3.474 369.0829_3.476 388.047_3.472 178.0818_3.476
## 2 26325.25 28839.441 3680.285 23033.50 71185.55
## 3 26661.69 22829.762 5441.328 45957.41 67737.33
## 4 11779.77 16705.210 3148.841 16806.27 42114.69
## 5 16950.73 19587.285 5100.924 14923.62 58070.16
## 6 5978.77 6944.293 6644.089 15894.61 21287.21
## 7 19562.04 19230.838 7792.771 28514.15 48103.16
## 357.1107_3.472 692.1776_3.461 860.2066_3.476 134.0612_3.478 178.0515_3.479
## 2 14011.925 500.0201 10398.271 48428.38 1932230.2
## 3 13775.351 502.2117 8563.597 43549.18 1811344.0
## 4 6658.996 1036.9456 4538.203 32288.12 1065876.6
## 5 12946.002 500.0000 7695.449 44112.54 1492137.8
## 6 5244.056 500.0225 1632.339 19090.24 576666.1
## 7 10987.581 502.2908 4974.250 37712.89 1247351.6
## 558.136_3.478 441.0613_3.482 574.1139_3.482 236.0095_3.482 775.1556_3.475
## 2 5825.826 21828.865 18109.123 39066.01 5742.722
## 3 3876.069 15824.353 13169.588 37001.30 4708.571
## 4 2554.707 16511.370 8449.400 42179.59 2568.109
## 5 4499.181 23779.107 13967.390 49828.35 4879.240
## 6 1612.539 7390.145 2859.680 27509.89 1227.612
## 7 2306.604 10609.488 5463.686 25460.79 2693.243
## 580.1303_3.48 379.091_3.484 590.1068_3.469 607.1267_3.483 371.0978_3.481
## 2 18088.844 141366.02 1108.0483 1024.011 5079.835
## 3 15281.735 119121.75 1563.5682 1148.368 5834.833
## 4 11567.551 92462.88 500.2725 1131.444 7468.166
## 5 21576.766 158943.12 1351.7960 1201.111 2381.384
## 6 3191.261 39452.00 500.0225 1003.463 3133.870
## 7 6442.870 70578.82 1194.7349 1051.424 5505.605
## 437.0497_3.488 393.0776_3.464 298.0002_3.49 395.091_3.521 178.0526_3.481
## 2 8622.992 3256.770 47037.97 2911.512 1932230.2
## 3 7041.377 2940.252 42788.46 2672.569 1811344.0
## 4 8870.491 2948.692 43298.94 3203.764 1065876.6
## 5 11053.334 2463.968 59809.56 2305.755 1492137.8
## 6 4157.979 2987.531 29050.88 2508.193 576666.1
## 7 3944.371 3215.432 30930.81 5999.976 1247351.6
## 246.0383_3.488 299.0779_3.509 412.1044_3.523 640.1347_3.515 359.1345_3.504
## 2 92775.07 2514.067 1316.682 1059.8517 7766.533
## 3 75916.83 5905.496 1395.975 502.2117 10761.731
## 4 97662.73 4428.641 1712.978 500.2725 9616.301
## 5 113493.88 2112.329 1042.024 500.0000 7535.427
## 6 72250.68 3174.472 1563.050 500.0225 10072.250
## 7 59849.73 4038.055 1918.717 502.2908 31457.898
## 396.1297_3.506 242.0128_3.535 399.1658_3.513 432.1631_3.52 666.2108_3.475
## 2 1237.8220 15092.338 14871.461 500.0201 1416.0035
## 3 1081.2500 7215.185 29974.570 1380.7079 1618.9263
## 4 500.2725 4597.500 5545.925 500.2725 1036.3748
## 5 500.0000 3201.710 20128.006 500.0000 500.0000
## 6 500.0225 4437.150 7750.137 5089.0770 500.0225
## 7 502.2908 2932.089 14633.692 502.2908 1110.3527
## 251.0128_3.524 404.0405_3.511 279.0487_3.569 241.1189_3.536 150.0562_3.542
## 2 1514.078 1378.107 1180.4450 39766.30 1171.8206
## 3 1187.021 2491.653 502.2117 95559.96 1434.4391
## 4 1249.939 1597.454 1334.9155 77710.12 500.2725
## 5 2018.757 1505.133 1316.9280 11695.77 1310.9160
## 6 2546.018 1509.680 3898.3310 35093.35 500.0225
## 7 8027.596 2086.812 1216.1870 43619.86 2278.5050
## 287.0227_3.548 507.0806_3.582 565.0578_3.576 411.0792_3.55 489.2111_3.578
## 2 41345.473 1448.2427 1177.1458 1696.945 500.0201
## 3 15155.293 502.2117 1004.4235 1300.895 6157.4854
## 4 27982.443 500.2725 1231.4302 1417.074 1008.0990
## 5 3729.891 500.0000 500.0000 1033.423 500.0000
## 6 2974.843 500.0225 500.0225 1345.010 500.0225
## 7 70927.520 502.2908 1217.3944 1259.521 502.2908
## 337.1469_3.591 194.0459_3.542 913.214_3.531 338.0879_3.557 622.0984_3.522
## 2 500.0201 4766.757 1478.5022 23926.635 500.0201
## 3 502.2117 8600.862 1213.0704 6397.518 502.2117
## 4 1047.5446 6345.964 500.2725 2405.257 500.2725
## 5 500.0000 7711.624 1249.4290 500.000 500.0000
## 6 1246.6431 10094.892 500.0225 2597.229 500.0225
## 7 502.2908 18734.629 502.2908 2083.749 502.2908
## 559.0435_3.584 230.0128_3.569 331.0121_3.56 1141.2436_3.619 428.0976_3.601
## 2 1786.7450 126730.470 1126.191 500.0201 500.0201
## 3 1297.4121 38399.640 1699.872 502.2117 502.2117
## 4 500.2725 4776.578 1268.722 500.2725 500.2725
## 5 1050.1140 1810.725 1482.222 500.0000 1198.9210
## 6 500.0225 4558.343 3006.193 500.0225 500.0225
## 7 502.2908 7235.931 1488.005 502.2908 502.2908
## 461.0863_3.571 891.2154_3.643 364.0958_3.594 680.165_3.608 255.1349_3.614
## 2 1315.324 500.0201 500.0201 500.0201 22303.91
## 3 1294.743 502.2117 502.2117 502.2117 46002.72
## 4 1070.920 500.2725 500.2725 500.2725 36518.00
## 5 1135.873 500.0000 500.0000 500.0000 8328.17
## 6 1177.447 500.0225 500.0225 500.0225 21621.37
## 7 1189.293 502.2908 1071.6423 502.2908 11909.59
## 495.2229_3.594 571.1469_3.618 556.1624_3.635 525.0334_3.606 322.1105_3.605
## 2 500.0201 500.0201 500.0201 12501.2710 500.0201
## 3 502.2117 1323.5890 502.2117 7867.9565 502.2117
## 4 1286.0040 1080.4750 500.2725 500.2725 500.2725
## 5 500.0000 500.0000 500.0000 500.0000 500.0000
## 6 500.0225 1036.8142 500.0225 1096.4861 1731.0074
## 7 1106.2970 1189.7152 1236.0323 1140.3828 502.2908
## 393.0495_3.605 427.197_3.64 383.0464_3.601 295.1298_3.641 585.0184_3.643
## 2 17994.143 3006.554 1747.976 19796.130 1617.0715
## 3 11281.469 5215.837 2125.001 7887.587 15123.0410
## 4 1547.240 1750.565 2248.891 7556.402 500.2725
## 5 2493.144 2250.605 1350.122 16807.713 1344.9110
## 6 1083.014 2734.360 1515.976 7550.448 3683.7324
## 7 3176.760 3191.191 6360.287 25827.354 1398.2491
## 583.0211_3.647 609.0726_3.66 607.0749_3.657 611.069_3.647 250.0718_3.655
## 2 1251.8810 500.0201 1151.8369 1277.2717 20649.060
## 3 13226.6140 1189.5818 1275.2057 502.2117 5720.161
## 4 500.2725 500.2725 500.2725 500.2725 10667.295
## 5 1067.1320 500.0000 500.0000 500.0000 11148.359
## 6 4161.7770 500.0225 500.0225 500.0225 10379.870
## 7 1614.9852 502.2908 1095.3780 1317.0248 9006.942
## 379.1607_3.652 336.1323_3.647 819.2194_3.7 203.0017_3.661 279.049_3.659
## 2 16945.36 500.0201 500.0201 45271.33 1586.7673
## 3 24049.40 502.2117 502.2117 160459.33 502.2117
## 4 10625.89 500.2725 500.2725 54059.41 1353.7037
## 5 13827.35 500.0000 500.0000 71851.03 1221.4770
## 6 12091.85 4356.7935 500.0225 38877.69 11208.2470
## 7 40916.52 1095.0127 502.2908 44831.32 1226.9275
## 1303.2955_3.702 413.144_3.692 269.0163_3.668 1003.2656_3.716 222.0406_3.686
## 2 500.0201 1493.285 500.0201 500.0201 5729.833
## 3 502.2117 1677.730 502.2117 502.2117 4179.101
## 4 500.2725 1590.604 500.2725 500.2725 4495.761
## 5 500.0000 1199.070 500.0000 500.0000 6324.002
## 6 500.0225 1636.885 4438.7334 500.0225 4578.976
## 7 502.2908 2880.227 1152.2068 502.2908 6489.992
## 607.1628_3.711 302.1145_3.69 1069.2473_3.702 841.2146_3.693 194.0458_3.657
## 2 500.0201 91857.836 500.0201 500.0201 5241.928
## 3 1142.7068 28181.780 502.2117 502.2117 8256.830
## 4 500.2725 15895.030 500.2725 500.2725 8486.932
## 5 500.0000 25076.066 500.0000 500.0000 7414.563
## 6 1118.8005 18015.004 500.0225 500.0225 10004.294
## 7 1185.6836 8015.856 502.2908 502.2908 15292.333
## 835.1973_3.718 334.0517_3.708 638.0934_3.724 374.1587_3.706 295.0806_3.721
## 2 500.0201 500.0201 500.0201 500.0201 2551.527
## 3 502.2117 502.2117 502.2117 502.2117 1578.585
## 4 500.2725 500.2725 500.2725 500.2725 6792.646
## 5 500.0000 500.0000 500.0000 500.0000 1362.611
## 6 500.0225 500.0225 500.0225 1364.5600 1525.805
## 7 502.2908 502.2908 502.2908 502.2908 2028.173
## 432.1213_3.706 997.2507_3.723 569.1307_3.754 373.1141_3.685 246.0753_3.721
## 2 1086.0332 500.0201 1258.921 8083.200 8153.134
## 3 502.2117 502.2117 1486.820 8978.245 3739.386
## 4 500.2725 500.2725 1545.027 9669.385 13370.054
## 5 500.0000 500.0000 500.000 4972.398 5864.395
## 6 500.0225 500.0225 1154.325 41840.973 7869.123
## 7 1022.6482 502.2908 2706.123 18099.877 6733.293
## 443.0945_3.705 277.0353_3.723 785.1891_3.723 800.1466_3.728 471.0822_3.708
## 2 1024.464 1568.938 500.0201 500.0201 1299.667
## 3 1124.501 1134.814 502.2117 502.2117 1479.687
## 4 1109.620 1435.755 500.2725 500.2725 1441.315
## 5 1000.000 1656.028 500.0000 500.0000 1001.342
## 6 1027.078 2858.177 500.0225 500.0225 2482.244
## 7 1072.291 1298.394 502.2908 502.2908 1807.228
## 1159.3041_3.727 1279.2478_3.734 747.2354_3.721 883.1453_3.774 1165.3189_3.729
## 2 500.0201 500.0201 500.0201 500.0201 500.0201
## 3 502.2117 502.2117 502.2117 502.2117 502.2117
## 4 500.2725 500.2725 500.2725 500.2725 500.2725
## 5 500.0000 500.0000 500.0000 500.0000 500.0000
## 6 500.0225 500.0225 500.0225 500.0225 500.0225
## 7 502.2908 502.2908 502.2908 502.2908 502.2908
## 493.0631_3.69 769.2167_3.733 801.154_3.74 373.1514_3.715 439.1608_3.725
## 2 1971.857 500.0201 500.0201 1381.096 5220.843
## 3 1739.183 502.2117 502.2117 1860.411 5158.259
## 4 2370.022 500.2725 500.2725 1082.327 2868.059
## 5 1546.894 500.0000 500.0000 1389.819 10824.269
## 6 4162.458 500.0225 500.0225 2253.506 6877.070
## 7 3055.104 502.2908 1040.0641 2120.341 6261.796
## 259.129_3.733 607.1633_3.738 245.0489_3.748 209.0532_3.773 197.0471_3.745
## 2 7273.879 500.0201 1777.117 500.0201 500.0201
## 3 4108.916 502.2117 7832.062 502.2117 502.2117
## 4 3302.068 500.2725 2226.311 1227.0742 500.2725
## 5 2128.313 500.0000 5338.036 1355.7790 500.0000
## 6 2416.147 1118.8005 6897.765 7695.1580 5006.8410
## 7 33639.645 1211.9608 5880.084 1023.0393 502.2908
## 209.0455_3.749 583.1089_3.742 370.0981_3.747 431.1141_3.739 605.1484_3.739
## 2 500.0201 2004.865 17987.980 2087.0034 500.0201
## 3 502.2117 1863.595 18174.686 1372.4535 502.2117
## 4 1227.0742 1008.516 11760.441 500.2725 500.2725
## 5 500.0000 1235.126 5629.892 1137.0180 500.0000
## 6 7695.1580 2917.747 10777.829 1138.9178 500.0225
## 7 1188.3182 2553.231 28153.053 1179.0244 1004.5817
## 1003.2655_3.721 447.0928_3.777 889.161_3.783 197.0453_3.76 979.2122_3.736
## 2 500.0201 5817.318 500.0201 500.0201 500.0201
## 3 502.2117 2607.914 1028.1815 502.2117 502.2117
## 4 500.2725 1321.883 500.2725 500.2725 500.2725
## 5 500.0000 1646.469 500.0000 500.0000 500.0000
## 6 500.0225 1304.611 500.0225 5006.8410 500.0225
## 7 502.2908 1368.090 502.2908 502.2908 502.2908
## 593.1471_3.747 429.082_3.756 417.0951_3.717 109.0288_3.757 336.072_3.762
## 2 500.0201 69880.9400 1424.3392 500.0201 24309.22
## 3 502.2117 38860.0230 1077.8129 502.2117 18206.63
## 4 500.2725 500.2725 500.2725 500.2725 17938.76
## 5 500.0000 1068.7960 500.0000 500.0000 10814.51
## 6 500.0225 1000.0450 500.0225 2511.1543 12824.37
## 7 1070.0051 502.2908 1817.1829 502.2908 16711.30
## 639.0783_3.758 497.0695_3.785 370.0972_3.765 243.1344_3.787 279.0984_3.799
## 2 6891.4270 13639.467 10709.796 79573.85 1330.325
## 3 6559.9937 5731.856 18174.686 29120.03 3223.249
## 4 500.2725 1049.104 11760.441 72831.16 5079.844
## 5 500.0000 500.000 5629.892 13209.66 1466.146
## 6 500.0225 1066.250 10777.829 38053.12 1446.676
## 7 502.2908 1435.978 28153.053 17992.34 1575.172
## 1108.0095_3.804 585.0051_3.809 810.9784_3.814 816.9958_3.812 618.1838_3.802
## 2 44856.06 2189.381 2182.8752 2483.237 500.0201
## 3 58556.51 10752.095 19287.7400 21774.906 502.2117
## 4 45225.29 1067.443 500.2725 1575.506 1030.1608
## 5 43306.58 500.000 500.0000 500.000 500.0000
## 6 57640.11 1693.049 1708.6609 2472.715 500.0225
## 7 47952.07 5656.994 7793.5156 12349.923 1097.2543
## 633.1142_3.808 600.982_3.815 649.0893_3.836 655.1071_3.826 883.1412_3.819
## 2 500.0201 5734.712 1169.6640 1332.0315 500.0201
## 3 502.2117 37533.940 502.2117 502.2117 502.2117
## 4 500.2725 3115.715 1093.6445 1044.4434 500.2725
## 5 500.0000 1094.854 500.0000 500.0000 500.0000
## 6 500.0225 4487.034 1229.8688 500.0225 500.0225
## 7 1303.1267 19456.430 1350.6036 1026.3260 502.2908
## 606.9991_3.815 458.9733_3.815 412.977_3.815 399.0007_3.816 421.0558_3.818
## 2 12853.463 12328.325 15588.761 22371.06 1479.353
## 3 69780.420 37161.800 22942.070 47564.72 1171.312
## 4 4775.146 8345.383 11561.022 15273.72 1430.515
## 5 2633.967 4435.841 8146.525 10219.75 1229.504
## 6 11777.096 12020.802 15848.546 23609.78 12992.240
## 7 39775.266 21458.223 22016.260 39299.32 1292.766
## 625.0354_3.814 107.0502_3.816 397.0034_3.815 189.0027_3.815 306.9561_3.814
## 2 1024.6462 36762.26 198955.55 103996.80 34684.13
## 3 1007.0374 67776.02 467526.16 228851.75 47803.57
## 4 500.2725 21243.68 119134.77 72777.19 32122.13
## 5 1035.6040 17190.75 68001.51 55028.70 23527.71
## 6 2980.5059 32108.36 195411.72 106728.00 36639.86
## 7 1322.2114 50042.43 370974.90 167055.02 43562.57
## 187.0079_3.815 631.0532_3.815 124.0486_3.843 859.0857_3.824 459.093_3.801
## 2 2834762 500.0201 500.0201 500.0201 33601.520
## 3 5661702 502.2117 502.2117 1100.2384 19828.346
## 4 1783194 500.2725 500.2725 500.2725 1412.816
## 5 1285458 1042.2290 500.0000 500.0000 1224.051
## 6 2722107 2436.1067 3670.5625 500.0225 1185.442
## 7 4625648 1033.1272 502.2908 502.2908 1568.900
## 187.0387_3.806 108.0216_3.832 208.0615_3.836 230.9963_3.838 211.0613_3.866
## 2 95299.80 500.0201 2304.320 1002.712 4237.934
## 3 120844.62 502.2117 5911.168 13124.798 4617.028
## 4 68292.05 500.2725 3792.178 24363.030 6673.512
## 5 50560.73 500.0000 8967.401 77042.875 3417.560
## 6 99994.70 5548.4710 5751.253 2117.499 76985.880
## 7 127672.47 502.2908 5121.352 42761.004 5618.117
## 336.0719_3.763 212.0937_3.815 211.0971_3.846 187.0101_3.824 123.0452_3.87
## 2 24309.22 1598.594 1665.1260 2834762.50 1613.408
## 3 18206.63 1042.977 502.2117 28989.66 1807.685
## 4 17938.76 1875.553 1493.0270 37131.57 2024.484
## 5 10814.51 1206.836 1288.0490 1285457.80 1806.485
## 6 12824.37 1691.985 2621.9620 2722107.00 33680.566
## 7 16711.30 1140.214 2047.3496 4625648.50 1728.097
## 403.066_3.865 907.1941_3.871 673.1446_3.853 333.0066_3.893 476.0359_3.919
## 2 500.0201 500.0201 500.0201 17232.445 1494.551
## 3 3756.6787 502.2117 1089.0626 47056.836 1867.046
## 4 500.2725 500.2725 500.2725 1098.178 1778.471
## 5 500.0000 500.0000 500.0000 1530.248 500.000
## 6 1015.0013 500.0225 500.0225 2223.325 1605.403
## 7 6201.0117 502.2908 1034.3046 1572.059 1912.628
## 446.1142_3.879 679.1625_3.856 913.2118_3.895 461.0857_3.898 335.0215_3.886
## 2 500.0201 500.0201 500.0201 1110.2135 10626.485
## 3 502.2117 502.2117 502.2117 502.2117 12157.343
## 4 500.2725 500.2725 500.2725 500.2725 1012.259
## 5 500.0000 500.0000 500.0000 500.0000 1066.522
## 6 1101.0021 500.0225 500.0225 1463.9512 1071.433
## 7 502.2908 502.2908 502.2908 1278.7991 1256.354
## 295.0252_3.897 264.0876_3.875 657.1659_3.876 445.1072_3.893 217.0173_3.88
## 2 1162.778 17601.15 500.0201 1099.715 20278.488
## 3 1033.216 23898.58 502.2117 2238.278 33048.547
## 4 1106.029 10057.52 500.2725 1164.713 7707.624
## 5 1192.085 8180.38 500.0000 500.000 13451.042
## 6 2119.468 10463.48 500.0225 2895.344 6215.372
## 7 1432.736 37543.09 1183.1326 1339.432 10430.682
## 351.07_3.888 431.0979_3.907 751.1822_3.898 283.093_3.891 363.021_3.933
## 2 19412.940 63325.5740 500.0201 10465.223 6603.633
## 3 31765.062 17252.3400 502.2117 12388.627 35801.215
## 4 19264.607 500.2725 500.2725 7955.655 1812.111
## 5 6007.056 500.0000 500.0000 3184.073 1472.760
## 6 24174.450 500.0225 500.0225 10926.524 1213.808
## 7 39197.200 1388.0386 502.2908 17390.200 1470.421
## 283.0822_3.893 517.1331_3.892 745.1653_3.9 368.0804_3.895 513.0964_3.899
## 2 94270.93 1225.8290 500.0201 4963.215 1226.3809
## 3 162427.92 502.2117 502.2117 21955.592 1745.1395
## 4 74429.41 500.2725 500.2725 16235.867 500.2725
## 5 27075.12 500.0000 500.0000 4350.932 1058.6010
## 6 117184.59 3785.7686 500.0225 10719.087 1888.6066
## 7 224133.02 1204.0728 502.2908 7148.158 1687.0594
## 217.1081_3.912 372.1116_3.914 434.1396_3.922 432.1447_3.927 331.1218_3.932
## 2 16106.48 7557.4870 500.0201 500.0201 500.0201
## 3 16787.77 1222.4287 502.2117 502.2117 4598.1577
## 4 13261.09 35055.9100 500.2725 500.2725 1406.8314
## 5 9372.49 13287.3630 1115.3230 500.0000 1032.9810
## 6 22671.48 500.0225 500.0225 1660.1782 500.0225
## 7 15859.53 1118.2866 1079.3500 1137.2874 2514.8323
## 237.0359_3.933 279.0491_3.95 167.0349_3.921 350.088_3.952 584.1374_3.972
## 2 500.0201 1542.510 1112.2850 65342.47 1059.7166
## 3 1216.0801 1401.980 502.2117 94949.50 502.2117
## 4 1077.5962 1434.365 1655.5316 79700.31 500.2725
## 5 1132.0750 1422.325 1029.5210 11276.09 500.0000
## 6 500.0225 26237.771 1593.4023 24731.63 1866.1840
## 7 1188.8030 1348.465 1957.2369 28154.83 1459.4160
## 210.0419_3.959 396.1279_4.011 1122.0249_4.032 337.1446_4.025 614.1849_4.02
## 2 500.0201 500.0201 19439.8140 1616.411 500.0201
## 3 502.2117 1576.9596 27973.2340 1111.945 502.2117
## 4 500.2725 500.2725 1021.6447 1552.321 500.2725
## 5 500.0000 500.0000 500.0000 500.000 500.0000
## 6 500.0225 500.0225 500.0225 1285.737 500.0225
## 7 502.2908 1235.6437 502.2908 1302.736 502.2908
## 427.197_4.049 194.0491_4.031 662.1454_4.018 514.136_3.993 428.0955_4.037
## 2 3337.395 14809.88 500.0201 1812.840 500.0201
## 3 6070.519 20284.23 502.2117 3762.163 1061.6772
## 4 2754.905 17819.64 500.2725 2912.229 500.2725
## 5 3383.081 20059.43 500.0000 1272.891 500.0000
## 6 3190.086 27963.54 500.0225 1481.690 1196.3405
## 7 4479.489 47632.62 502.2908 1670.442 502.2908
## 150.056_4.032 656.1287_4.025 216.0277_4.034 194.0459_4.033 313.995_4.031
## 2 1134.012 500.0201 1784.063 14809.88 1360.275
## 3 1613.357 502.2117 1692.067 20284.23 2456.665
## 4 1438.176 500.2725 2174.269 17819.64 2465.706
## 5 1544.032 500.0000 2707.154 20059.43 1854.125
## 6 2385.166 500.0225 2134.502 27963.54 2813.499
## 7 3688.293 502.2908 4192.884 47632.62 4045.391
## 678.1113_4.032 411.0759_4.059 281.1142_4.039 429.0815_4.032 606.1191_4.053
## 2 500.0201 3425.755 12571.518 42738.9300 1031.2367
## 3 502.2117 3658.321 13000.793 33700.7800 1118.6216
## 4 500.2725 3619.039 14959.941 500.2725 1032.2228
## 5 500.0000 2601.438 7128.849 500.0000 1012.1950
## 6 500.0225 2628.296 11598.580 1191.8750 500.0225
## 7 502.2908 1806.507 11206.693 1244.0428 502.2908
## 433.0646_4.045 373.1139_4.04 495.223_4.067 255.1349_4.042 379.1607_4.047
## 2 6830.757 15089.266 1794.724 43546.05 30632.40
## 3 5472.275 14828.409 1200.680 115880.21 43233.90
## 4 1083.841 20148.734 1311.696 92772.07 17412.69
## 5 500.000 7662.456 1129.322 13672.81 20755.98
## 6 1128.617 15213.119 1113.566 43302.28 21854.06
## 7 1092.262 28159.100 13910.304 21668.38 80555.91
## 401.1434_4.052 184.0979_4.054 571.1476_4.081 201.0227_4.086 417.1173_4.046
## 2 8576.299 8380.165 500.0201 276003.060 1107.0532
## 3 10649.242 15776.373 502.2117 566404.600 1265.0997
## 4 4732.882 5861.940 1209.0028 2771.844 500.2725
## 5 5780.745 22387.355 500.0000 2410.425 500.0000
## 6 6853.776 10352.370 500.0225 2831.072 1369.1118
## 7 18687.470 10717.224 1363.8920 2442.787 1511.8750
## 351.1236_4.04 562.1922_4.035 397.1911_4.067 367.2236_4.071 447.0921_4.067
## 2 1080.4198 500.0201 500.0201 18045.54 8853.170
## 3 502.2117 1210.4100 502.2117 25799.00 9127.329
## 4 1200.1506 1155.6400 500.2725 18495.12 1358.094
## 5 500.0000 500.0000 500.0000 10054.54 16708.617
## 6 1494.5300 500.0225 500.0225 21946.88 5332.641
## 7 1529.7859 1544.2903 502.2908 30512.05 6793.316
## 375.1294_4.074 181.0507_4.062 297.0975_4.072 489.2123_4.076 201.0227_4.09
## 2 9512.906 500.0201 118699.695 500.0201 276003.060
## 3 8880.499 502.2117 97780.750 12474.1380 566404.600
## 4 12815.970 1146.7318 1337.311 1802.8375 2771.844
## 5 10566.450 1582.5880 1780.940 500.0000 2410.425
## 6 11239.810 9212.3840 1384.641 500.0225 2831.072
## 7 19962.280 2154.8665 2276.986 1123.0166 2442.787
## 765.197_4.127 365.0863_4.095 680.2306_4.136 151.04_4.082 853.2516_4.121
## 2 1007.2653 23400.213 500.0201 2087.742 500.0201
## 3 502.2117 20392.602 502.2117 5241.445 502.2117
## 4 500.2725 1099.246 500.2725 5335.681 500.2725
## 5 500.0000 1189.499 500.0000 2476.194 500.0000
## 6 500.0225 1167.147 500.0225 2929.061 500.0225
## 7 1107.9886 1638.791 502.2908 2539.698 1379.6024
## 165.0557_4.102 204.0666_4.129 681.2357_4.115 247.0798_4.12 908.2639_4.165
## 2 15463.088 16845.307 1350.4375 1013.452 500.0201
## 3 14762.573 5050.506 502.2117 1824.505 502.2117
## 4 6505.936 9398.247 500.2725 2595.512 500.2725
## 5 5094.479 6261.242 500.0000 5533.848 500.0000
## 6 6902.454 7298.995 500.0225 1768.244 500.0225
## 7 81986.080 8835.773 502.2908 5627.492 502.2908
## 185.0819_4.129 246.0752_4.139 531.1447_4.141 497.2379_4.163 1122.0248_4.125
## 2 31914.40 9816.386 1195.221 1588.184 18176.4900
## 3 31678.77 5029.870 1455.278 1170.089 31517.2850
## 4 39332.91 15328.499 1152.678 1633.145 500.2725
## 5 20293.20 6565.389 1076.325 1250.345 500.0000
## 6 43057.23 9835.374 1312.184 1418.890 500.0225
## 7 54471.06 8589.885 1625.801 3376.248 502.2908
## 845.2477_4.186 363.1658_4.172 418.1327_4.164 565.0619_4.188 569.1299_4.178
## 2 500.0201 7593.479 500.0201 1276.8286 1245.6401
## 3 502.2117 9047.312 502.2117 2187.6565 1562.2822
## 4 500.2725 12075.439 500.2725 500.2725 500.2725
## 5 500.0000 11406.414 500.0000 500.0000 1309.7360
## 6 1145.1302 38251.996 500.0225 3525.0964 1004.5967
## 7 1203.7396 78509.090 502.2908 1854.6865 1456.9252
## 245.0488_4.187 675.1511_4.19 691.3179_4.171 813.255_4.198 345.1933_4.201
## 2 3896.805 500.0201 1084.600 500.0201 1314.321
## 3 4913.752 1018.6769 1226.086 502.2117 1571.962
## 4 3427.643 500.2725 1213.078 500.2725 1963.884
## 5 13658.554 500.0000 1055.245 500.0000 1145.560
## 6 26215.668 500.0225 1061.545 500.0225 2622.921
## 7 20793.863 502.2908 13143.588 1297.0577 19960.688
## 1041.2898_4.199 579.2066_4.211 714.3024_4.193 710.0906_4.208 461.0817_4.193
## 2 500.0201 1096.4995 500.0201 500.0201 1559.1384
## 3 502.2117 502.2117 502.2117 502.2117 502.2117
## 4 500.2725 1189.3319 500.2725 500.2725 1018.1447
## 5 500.0000 500.0000 500.0000 500.0000 1307.6060
## 6 500.0225 4442.6600 500.0225 500.0225 1383.2630
## 7 502.2908 1456.6451 8200.0220 502.2908 1259.8547
## 285.0968_4.206 211.0967_4.185 431.1146_4.185 913.2113_4.211 485.0499_4.198
## 2 1591.004 2141.010 1492.357 500.0201 500.0201
## 3 1198.283 1366.050 2108.565 502.2117 1066.2404
## 4 1347.896 1686.096 1187.821 500.2725 500.2725
## 5 1243.912 1193.337 500.000 500.0000 500.0000
## 6 2118.441 6931.248 1646.158 500.0225 1221.0613
## 7 1457.149 1860.470 2051.848 502.2908 1110.7401
## 807.2393_4.206 413.1426_4.219 137.0244_4.225 891.2172_4.205 1141.2441_4.201
## 2 500.0201 2777.509 1388.574 500.0201 500.0201
## 3 502.2117 14651.119 1873.621 502.2117 502.2117
## 4 500.2725 7951.314 1760.398 500.2725 500.2725
## 5 500.0000 4579.056 2756.803 500.0000 500.0000
## 6 500.0225 13593.566 3097.170 500.0225 500.0225
## 7 1009.4717 72335.586 4428.384 502.2908 502.2908
## 673.1455_4.198 476.0415_4.203 465.0845_4.208 123.0452_4.211 445.1115_4.233
## 2 500.0201 500.0201 1415.134 2133.562 1439.395
## 3 502.2117 1049.3372 2255.649 1047.725 2993.442
## 4 500.2725 500.2725 1904.991 2364.568 1467.602
## 5 500.0000 500.0000 1205.233 1380.799 1369.613
## 6 500.0225 500.0225 2061.030 97200.336 14130.642
## 7 502.2908 502.2908 6454.032 1342.152 2321.446
## 212.0968_4.21 108.0218_4.217 907.1956_4.201 679.1604_4.197 892.2207_4.211
## 2 500.0201 500.0201 500.0201 1211.000 500.0201
## 3 502.2117 502.2117 502.2117 1610.286 502.2117
## 4 500.2725 500.2725 500.2725 1168.610 1006.4688
## 5 500.0000 500.0000 500.0000 500.000 500.0000
## 6 1060.3683 15684.2350 500.0225 1703.815 500.0225
## 7 502.2908 502.2908 502.2908 1724.758 502.2908
## 689.1166_4.213 488.0429_4.206 657.1681_4.216 815.077_4.202 1135.2278_4.215
## 2 500.0201 500.0201 1088.0382 500.0201 500.0201
## 3 1767.0101 502.2117 502.2117 502.2117 502.2117
## 4 500.2725 500.2725 500.2725 500.2725 500.2725
## 5 500.0000 500.0000 500.0000 500.0000 500.0000
## 6 500.0225 500.0225 500.0225 500.0225 500.0225
## 7 502.2908 502.2908 502.2908 502.2908 502.2908
## 447.092_4.187 559.0453_4.234 347.1678_4.233 361.1501_4.225 1132.0633_4.207
## 2 14209.750 500.0201 1978.942 18063.72 500.0201
## 3 6538.014 502.2117 4885.007 48543.94 502.2117
## 4 1221.749 500.2725 6896.696 13687.78 500.2725
## 5 9892.394 1210.5060 4696.693 12739.07 500.0000
## 6 3765.201 2102.8110 5900.621 17000.66 2203.8574
## 7 4402.091 1061.7600 20057.316 31987.84 502.2908
## 439.1607_4.307 466.0888_4.204 401.1094_4.24 503.2011_4.23 211.0615_4.235
## 2 35630.74 1155.831 1356.311 500.0201 9299.780
## 3 23377.72 1212.071 1017.250 502.2117 5661.752
## 4 32249.98 1130.323 1140.907 1004.0062 14144.817
## 5 23424.88 500.000 1192.040 500.0000 6879.601
## 6 20375.06 1008.788 12783.143 2787.1323 222310.810
## 7 48064.66 2062.616 1082.377 1340.5103 11281.446
## 295.0815_4.242 363.0692_4.266 269.1506_4.275 481.0163_4.286 361.0666_4.291
## 2 5294.975 1719.503 55917.74 500.0201 500.0201
## 3 1994.145 1519.192 56339.68 1070.1798 502.2117
## 4 12367.124 3087.034 106905.86 1194.2478 1052.2242
## 5 1849.375 1509.568 61634.27 1139.4370 1005.2570
## 6 1354.144 1696.741 65104.31 500.0225 1268.7782
## 7 2633.776 2583.050 97614.09 1564.8662 1169.2520
## 459.0361_4.296 745.1266_4.269 429.0571_4.306 265.1192_4.316 449.1074_4.301
## 2 1225.607 500.0201 500.0201 13066.91 5779.1270
## 3 1258.092 502.2117 502.2117 15573.86 4929.6520
## 4 1099.779 500.2725 500.2725 22505.95 1634.0016
## 5 1185.275 500.0000 1181.8360 18379.25 1670.1820
## 6 1223.404 500.0225 500.0225 14431.56 500.0225
## 7 1103.347 502.2908 1193.2788 30736.55 1889.6991
## 199.0068_4.328 333.0047_4.33 459.1504_4.33 492.1884_4.319 252.0891_4.335
## 2 22453.934 40354.176 27175.84 500.0201 500.0201
## 3 32792.887 159986.280 16161.56 502.2117 502.2117
## 4 123501.590 1782.307 9640.47 500.2725 500.2725
## 5 44842.332 1665.914 11853.27 500.0000 500.0000
## 6 7873.260 2017.997 13427.35 500.0225 500.0225
## 7 9935.031 2137.633 11292.57 502.2908 502.2908
## 572.2528_4.339 197.0819_4.344 363.1657_4.345 359.1344_4.351 229.017_4.354
## 2 17158.768 27600.63 13217.099 2929.514 3282.877
## 3 5759.800 18742.19 6192.381 10011.053 25208.588
## 4 4977.178 37192.69 10307.217 4351.089 10712.559
## 5 4262.561 16994.82 3932.929 1805.749 12468.570
## 6 12581.195 42092.09 12115.064 3142.570 16434.314
## 7 13409.015 47107.39 9359.776 20665.857 16348.319
## 385.1685_4.355 295.0242_4.354 335.0244_4.36 640.3332_4.371 243.0329_4.371
## 2 27485.650 1037.856 30748.283 5514.133 2071.189
## 3 5134.586 1361.541 44077.810 6525.265 9016.120
## 4 22228.174 1694.041 1166.777 7909.386 4185.294
## 5 12024.319 1146.532 1948.247 6326.681 6490.809
## 6 41181.690 1123.145 1594.863 2940.448 5720.709
## 7 20923.797 1223.440 1316.059 12598.622 7917.560
## 446.1831_4.341 425.145_4.374 514.1681_4.367 343.0855_4.396 495.1174_4.369
## 2 1560.262 14722.687 500.0201 8483.258 3014.250
## 3 1121.181 4872.185 502.2117 28780.348 1872.900
## 4 1361.348 9586.896 1026.9316 8179.717 1532.400
## 5 500.000 11847.822 500.0000 24581.084 1267.245
## 6 8808.360 8369.748 2427.3208 5976.216 1544.000
## 7 1294.736 13045.540 1197.7728 12347.563 3046.042
## 217.0172_4.4 397.1895_4.429 533.1257_4.426 527.1086_4.425 455.1062_4.407
## 2 58557.633 500.0201 1439.5072 1117.903 1394.1925
## 3 47241.530 502.2117 1081.0302 1266.921 3071.6780
## 4 4373.019 1564.3976 500.2725 1091.927 500.2725
## 5 8832.745 500.0000 1340.5690 500.000 500.0000
## 6 3322.810 1122.5442 1077.5782 1105.840 500.0225
## 7 12286.225 1581.0319 1646.5192 1971.332 1037.0580
## 363.0234_4.453 517.0977_4.399 465.1366_4.417 397.1496_4.415 817.2886_4.438
## 2 14996.744 1303.6475 1261.2980 1728.568 500.0201
## 3 88644.150 502.2117 1157.9459 1439.837 502.2117
## 4 9155.059 1515.7781 500.2725 1454.498 500.2725
## 5 4044.874 500.0000 500.0000 1406.541 500.0000
## 6 3701.562 1636.8439 1123.7861 2502.454 500.0225
## 7 4041.289 1484.1147 2097.8298 3281.939 1516.2362
## 485.1661_4.43 361.0646_4.442 363.0701_4.451 429.0565_4.442 459.0924_4.452
## 2 28476.84 500.0201 1267.282 1903.0104 1083.5731
## 3 25053.10 502.2117 2514.663 502.2117 1466.3830
## 4 33872.00 500.2725 1640.809 1088.0776 500.2725
## 5 18330.00 1200.1620 1702.103 500.0000 1342.4900
## 6 21684.74 1011.9418 1719.399 500.0225 1197.8843
## 7 16546.48 1386.5675 1816.765 502.2908 1592.6484
## 391.0993_4.492 445.0782_4.484 396.1124_4.459 427.1588_4.465 405.1764_4.467
## 2 2134.001 36958.086 3297.005 18692.27 75606.05
## 3 1858.502 9651.558 8763.882 27626.54 105838.61
## 4 2473.137 1310.511 3563.590 10963.88 36379.30
## 5 1274.227 1040.282 2283.335 13559.53 50422.45
## 6 2064.223 1429.537 8070.898 13868.08 46699.69
## 7 1378.028 3928.955 10723.871 28537.16 130460.76
## 565.1557_4.485 243.0779_4.486 245.0937_4.487 417.1192_4.5 509.0959_4.5
## 2 1897.167 98969.26 34343.207 500.0201 1274.533
## 3 1021.917 52586.68 8035.021 502.2117 1197.657
## 4 1644.339 45230.14 6770.942 500.2725 1364.642
## 5 1152.575 43097.77 10609.345 500.0000 1022.691
## 6 7180.364 21415.42 5492.720 1072.8165 1218.374
## 7 1630.761 39697.03 7075.240 1442.2354 1625.541
## 212.0065_4.503 580.1557_4.511 297.1062_4.504 365.0866_4.514 297.0975_4.498
## 2 500.0201 500.0201 18571.6330 46667.790 265259.060
## 3 1022.4869 502.2117 17202.4800 35284.950 211687.700
## 4 500.2725 500.2725 500.2725 1740.001 1403.205
## 5 500.0000 500.0000 500.0000 1288.222 1367.149
## 6 500.0225 500.0225 1220.2224 1295.263 1423.828
## 7 1486.0110 502.2908 1410.9357 2355.257 2220.741
## 617.1836_4.515 501.1339_4.505 447.0919_4.504 181.0503_4.495 795.2377_4.518
## 2 6491.5957 1356.5575 12255.377 1029.6250 500.0201
## 3 4387.0020 1064.6024 12365.822 502.2117 1082.4540
## 4 500.2725 500.2725 1192.028 500.2725 500.2725
## 5 1129.4430 1015.4450 26084.623 1111.1550 500.0000
## 6 1008.0633 1118.3269 6507.131 15393.7630 500.0225
## 7 1075.1255 2011.5920 8644.871 1972.9110 502.2908
## 801.2549_4.519 549.1948_4.514 443.1248_4.497 345.1938_4.506 521.1082_4.508
## 2 500.0201 1080.6757 1314.985 1267.348 10740.661
## 3 4069.1458 1090.1663 4971.608 1438.247 13150.938
## 4 500.2725 1070.9548 1884.193 2342.148 1017.529
## 5 500.0000 500.0000 1631.061 1537.169 1401.632
## 6 500.0225 1173.4874 3531.442 2251.911 1400.605
## 7 502.2908 502.2908 17172.127 32250.795 1293.225
## 411.1279_4.49 411.1654_4.559 714.3028_4.511 692.32_4.516 833.2468_4.513
## 2 3198.321 2218.742 500.0201 500.0201 500.0201
## 3 1807.538 1645.820 502.2117 502.2117 1170.9766
## 4 1960.152 2130.835 500.2725 500.2725 500.2725
## 5 1357.588 3635.758 500.0000 500.0000 500.0000
## 6 2660.600 2767.225 500.0225 500.0225 500.0225
## 7 2799.556 5365.726 16415.5300 13039.1040 8833.0330
## 481.1298_4.503 713.2993_4.51 465.1045_4.511 475.1137_4.509 403.1133_4.514
## 2 1835.278 500.0201 1859.053 1847.537 1518.123
## 3 7389.533 2150.7368 11710.796 7959.361 6069.457
## 4 3776.654 1023.2011 5262.328 3925.934 3340.619
## 5 3245.185 500.0000 2839.534 1782.431 2240.783
## 6 6306.434 1622.5605 7581.456 4740.910 6226.267
## 7 28111.535 44106.5740 51713.290 23622.370 32393.154
## 691.3175_4.507 345.1554_4.506 779.2721_4.515 433.1136_4.519 792.3445_4.502
## 2 500.0201 20852.30 1000.0402 36876.105 500.0201
## 3 1424.4734 147361.27 502.2117 130782.100 502.2117
## 4 500.2725 63373.27 500.2725 1524.914 500.2725
## 5 500.0000 28485.64 500.0000 1178.540 500.0000
## 6 1198.5745 105147.38 1074.5065 2467.608 500.0225
## 7 34889.7770 779903.90 502.2908 5195.467 502.2908
## 413.1426_4.51 544.1472_4.514 345.1556_4.509 446.1795_4.499 814.3239_4.509
## 2 3822.189 1002.5149 20852.30 1210.685 500.0201
## 3 22333.023 502.2117 147361.27 1340.847 502.2117
## 4 12796.617 500.2725 63373.27 1146.634 500.2725
## 5 5930.739 500.0000 28485.64 500.000 500.0000
## 6 19394.152 1094.1141 105147.38 9838.250 500.0225
## 7 108875.230 502.2908 41562.10 1084.796 502.2908
## 572.2105_4.512 491.191_4.507 514.1698_4.543 341.1234_4.519 531.0773_4.532
## 2 500.0201 500.0201 1124.3052 3259.073 4715.769
## 3 502.2117 502.2117 502.2117 1429.310 12150.315
## 4 500.2725 500.2725 500.2725 1860.293 1904.449
## 5 500.0000 500.0000 500.0000 2248.730 1446.962
## 6 500.0225 500.0225 2525.5530 12178.781 1547.482
## 7 2657.9800 1205.8857 502.2908 22193.355 1471.421
## 389.0919_4.531 541.2651_4.526 385.1861_4.524 204.0666_4.532 272.0538_4.533
## 2 30853.84 49211.22 8278.135 56397.742 11972.399
## 3 24835.94 33827.01 9187.460 5907.105 1354.588
## 4 30947.13 41123.15 3388.366 31545.168 7493.142
## 5 21487.71 26071.28 13857.561 20137.030 4345.635
## 6 24255.96 45082.98 8629.143 22654.207 4862.695
## 7 11179.16 19061.70 9608.062 21858.170 5151.452
## 224.062_4.537 383.1529_4.559 425.0363_4.553 201.0228_4.555 203.0172_4.555
## 2 21967.370 27211.695 40519.293 1109838.100 49373.6520
## 3 5963.214 6209.749 163893.190 2740269.200 107369.7000
## 4 17189.030 17199.498 1391.366 8847.902 500.2725
## 5 9347.290 9585.461 500.000 4505.520 1247.9530
## 6 20445.396 34320.620 1161.300 8279.492 1348.0198
## 7 11412.685 10278.622 1670.541 4023.033 1271.0688
## 1122.0244_4.55 493.1666_4.573 366.0851_4.55 269.1614_4.571 270.1538_4.572
## 2 38332.2420 8883.677 11007.1720 12831.29 24905.38
## 3 48068.5940 12363.511 8576.7040 11171.72 20781.96
## 4 1629.8573 1725.057 500.2725 23895.34 48914.48
## 5 1559.2230 1870.743 1732.6070 13119.94 26293.60
## 6 1102.7025 2252.508 500.0225 10984.53 29178.05
## 7 502.2908 2590.352 4142.9850 18660.11 37244.04
## 269.1505_4.575 389.0981_4.564 561.29_4.571 151.04_4.576 536.1898_4.583
## 2 168327.2 11812.046 1980.800 1379.485 500.0201
## 3 131175.2 8569.879 1417.046 7649.901 502.2117
## 4 304923.7 22971.705 8845.783 12576.146 500.2725
## 5 166453.5 10959.376 2650.157 2479.001 500.0000
## 6 170655.0 11111.521 2065.576 3811.464 1092.3750
## 7 211573.1 12799.598 3872.688 3606.068 502.2908
## 355.1037_4.621 238.0776_4.607 507.2078_4.607 237.1243_4.614 550.2061_4.628
## 2 1749.600 11613.255 1238.812 6867.794 1039.6333
## 3 3452.048 3164.504 4206.136 18515.762 502.2117
## 4 2007.564 17256.932 5436.716 19349.865 500.2725
## 5 1279.937 8977.313 4871.195 25809.947 500.0000
## 6 5165.513 14343.507 11767.072 10065.170 500.0225
## 7 13623.087 7438.664 5104.551 45283.125 502.2908
## 482.2186_4.626 602.1667_4.638 618.1913_4.649 400.1429_4.669 440.1641_4.66
## 2 500.0201 500.0201 1001.4805 4314.131 22935.53
## 3 502.2117 502.2117 502.2117 5263.606 14449.48
## 4 500.2725 500.2725 500.2725 3461.448 18276.03
## 5 500.0000 500.0000 500.0000 2466.310 12178.72
## 6 500.0225 500.0225 500.0225 1867.316 12900.19
## 7 1340.7145 502.2908 502.2908 4643.139 31328.14
## 461.1426_4.66 439.1608_4.66 539.2494_4.677 477.1039_4.686 245.0485_4.677
## 2 22342.83 84061.96 52808.73 1336.4696 13326.772
## 3 13347.72 47145.14 41944.20 1394.1194 1610.615
## 4 18871.06 70785.08 45355.29 500.2725 6064.749
## 5 14962.76 50771.34 27440.58 500.0000 40403.617
## 6 13647.03 49392.33 38826.91 1266.9812 59262.850
## 7 29214.35 127771.74 16958.50 1292.3481 52591.430
## 395.1144_4.677 354.1007_4.717 473.1448_4.73 600.2843_4.732 339.1082_4.74
## 2 500.0201 3306.901 27960.912 81664.01 4630.577
## 3 502.2117 12077.505 2878.747 25238.91 3142.159
## 4 1031.8560 3519.626 10558.711 28730.54 5125.011
## 5 500.0000 2596.864 4601.004 32069.04 5948.137
## 6 500.0225 2501.928 1864.766 50578.04 5521.898
## 7 1044.7402 14006.885 17641.162 34181.21 16994.557
## 305.0561_4.737 690.3154_4.751 243.1236_4.74 175.0975_4.743 343.0855_4.765
## 2 500.0201 1196.3077 16059.15 500.0201 18579.60
## 3 1489.6871 6130.7104 12399.77 1557.9905 75148.27
## 4 1089.7163 5252.0000 18358.46 1878.2666 20522.63
## 5 1152.1880 500.0000 11756.49 500.0000 56479.83
## 6 500.0225 500.0225 20118.56 1349.3058 16951.32
## 7 1038.4114 5154.5100 56901.96 1206.2797 27130.47
## 220.0979_4.733 415.0625_4.788 347.0774_4.782 171.0487_4.824 541.2651_4.807
## 2 1638.4552 1021.1602 500.0201 500.0201 122914.78
## 3 1724.1215 502.2117 1041.0525 502.2117 75771.56
## 4 1005.2814 500.2725 500.2725 500.2725 88029.50
## 5 500.0000 500.0000 500.0000 500.0000 53759.32
## 6 500.0225 1044.3330 500.0225 500.0225 95655.71
## 7 1865.9061 502.2908 1173.3026 502.2908 38066.96
## 137.0243_4.815 495.2227_4.812 517.2049_4.804 565.1563_4.799 366.1016_4.846
## 2 1662.068 11146.928 2882.8188 2647.606 3199.090
## 3 3193.011 1366.409 502.2117 1756.952 23928.941
## 4 2641.717 2352.657 500.2725 1647.281 5646.136
## 5 5506.447 1095.126 500.0000 1374.983 7875.066
## 6 4906.688 1566.543 500.0225 17029.764 5112.749
## 7 8481.083 66939.670 17211.6200 1484.852 5723.152
## 411.2021_4.884 383.153_4.885 507.2079_4.889 433.113_4.886 553.0631_4.891
## 2 11232.75 70643.29 1886.963 102240.2200 9312.3955
## 3 17214.39 10736.60 7853.381 329724.4400 32912.5430
## 4 14588.95 29128.12 11057.503 500.2725 500.2725
## 5 15264.83 15596.14 5999.206 500.0000 500.0000
## 6 18982.31 71164.58 17203.416 1123.0288 500.0225
## 7 34991.22 16922.43 6678.316 1115.3750 1093.7258
## 427.1967_4.894 448.1966_4.911 541.265_4.928 493.228_4.95 357.1009_4.96
## 2 3329.276 500.0201 106122.69 1161.597 12913.057
## 3 3412.567 502.2117 86854.49 2668.999 9450.572
## 4 2824.127 500.2725 123232.44 4901.623 10193.088
## 5 3092.959 500.0000 50948.10 2959.201 5238.429
## 6 3632.721 500.0225 84515.42 4996.156 4483.346
## 7 10620.657 1370.2866 37987.12 2811.050 11063.640
## 433.2077_4.995 462.1766_4.998 407.0405_4.991 591.3184_5.021 294.149_5.025
## 2 50237.28 20843.97 1233.016 4959.5977 500.0201
## 3 65668.17 40955.99 1171.720 1760.4808 502.2117
## 4 23353.03 42852.52 1328.654 500.2725 500.2725
## 5 39260.46 14111.68 500.000 5026.2870 500.0000
## 6 34034.77 30898.18 1194.664 1887.0361 500.0225
## 7 136370.02 26898.54 1237.925 10924.7770 502.2908
## 442.1246_5.012 199.0975_5.017 198.1135_5.021 436.1064_5.004 403.1019_5.042
## 2 500.0201 31569.98 20392.40 500.0201 17884.021
## 3 502.2117 43998.17 24181.24 502.2117 1201.343
## 4 500.2725 28701.64 22313.44 500.2725 1341.471
## 5 500.0000 23920.47 31832.01 500.0000 1294.283
## 6 500.0225 45910.37 35357.32 500.0225 60392.465
## 7 1045.8989 75289.93 46829.72 502.2908 7142.374
## 374.1378_5.008 236.1018_5.033 354.1009_5.03 589.3024_5.037 253.0503_5.048
## 2 500.0201 500.0201 12059.978 1183.0342 87628.7900
## 3 502.2117 502.2117 38183.977 502.2117 9273.5380
## 4 500.2725 500.2725 9882.305 500.2725 500.2725
## 5 500.0000 1208.2830 8366.475 11970.8240 500.0000
## 6 500.0225 1103.7936 7548.289 5399.0730 500.0225
## 7 502.2908 1434.4534 49713.695 12103.9480 502.2908
## 593.3333_5.048 539.1147_5.072 377.1019_5.09 255.0674_5.078 509.2751_5.084
## 2 20812.2970 500.0201 1027.2092 25627.5120 12026.244
## 3 502.2117 1068.5839 1151.8346 2107.4490 8152.416
## 4 500.2725 1030.7670 500.2725 500.2725 16909.273
## 5 500.0000 1290.2890 500.0000 500.0000 11089.767
## 6 1264.2401 3301.1820 1162.5295 1099.6250 8110.630
## 7 2037.0231 1504.2439 2033.6578 1006.4058 10047.322
## 525.27_5.101 387.1658_5.117 544.2886_5.154 366.1015_5.18 221.1178_5.185
## 2 19517.926 22937.21 1040.8656 8198.072 500.0201
## 3 14197.836 20481.35 1061.0623 72130.984 502.2117
## 4 16485.213 41533.52 500.2725 19640.703 500.2725
## 5 5771.371 22260.06 500.0000 25038.250 500.0000
## 6 12939.461 23806.90 1118.5981 19572.678 1199.3672
## 7 7958.508 53824.02 502.2908 19473.297 502.2908
## 177.1283_5.202 213.1132_5.201 511.2544_5.206 502.1148_5.219 352.0859_5.224
## 2 500.0201 24307.09 5852.871 500.0201 112688.7
## 3 502.2117 33047.15 3714.066 502.2117 458347.9
## 4 500.2725 34331.59 11037.714 500.2725 262000.7
## 5 500.0000 23485.19 4631.938 500.0000 100043.3
## 6 500.0225 60858.36 4175.990 500.0225 208584.7
## 7 502.2908 31735.41 5967.039 502.2908 182137.0
## 218.0821_5.22 317.1602_5.227 385.1498_5.181 235.0957_5.223 492.1871_5.243
## 2 1588.379 2098.528 3198.562 5796.480 500.0201
## 3 17984.600 1498.562 2005.385 6726.168 502.2117
## 4 4179.013 1280.783 3541.440 7509.884 1018.1037
## 5 2000.520 1878.778 2268.223 5944.811 500.0000
## 6 4259.646 53313.613 12095.850 13241.854 500.0225
## 7 21668.537 3814.289 4596.590 6477.691 502.2908
## 283.1657_5.239 481.2439_5.242 549.2312_5.243 493.1858_5.275 601.2255_5.243
## 2 15629.707 96519.69 17937.154 1343.4958 1291.320
## 3 14522.388 46078.28 11311.921 1473.6250 1708.315
## 4 18881.834 92854.02 17842.053 500.2725 1841.151
## 5 4988.767 46593.10 11252.220 1008.9090 500.000
## 6 16454.770 47444.93 7793.190 1020.0914 1166.777
## 7 5726.347 23718.97 5081.666 1117.0500 1741.182
## 651.3856_5.28 537.1508_5.278 191.1105_5.283 223.0969_5.259 509.1453_5.277
## 2 2426.274 1347.7834 2221.5210 3237.114 1021.5819
## 3 22397.574 1166.1658 502.2117 3351.843 1093.4860
## 4 2509.852 500.2725 500.2725 3574.743 1220.8693
## 5 11791.816 1281.8350 500.0000 3418.265 500.0000
## 6 5622.452 1298.5032 2148.1370 5141.163 500.0225
## 7 2543.095 1387.5708 1963.0996 5629.794 1069.8583
## 257.0801_5.284 73.0294_5.277 471.199_5.262 515.1648_5.296 355.0445_5.237
## 2 1097.2024 500.0201 500.0201 1029.4464 1697.070
## 3 502.2117 1118.5565 502.2117 1324.2821 2313.245
## 4 500.2725 500.2725 500.2725 500.2725 2196.776
## 5 500.0000 500.0000 500.0000 500.0000 1403.233
## 6 1577.2256 1048.8716 1329.5245 500.0225 4114.302
## 7 1487.7784 502.2908 1528.0974 1205.0000 1984.349
## 191.1076_5.282 433.2076_5.283 223.0982_5.267 531.1311_5.287 536.3209_5.3
## 2 2221.5210 19980.69 2078.336 500.0201 500.0201
## 3 502.2117 22105.52 3351.843 502.2117 502.2117
## 4 500.2725 15200.38 1130.048 1148.1954 500.2725
## 5 500.0000 13286.03 3203.035 1093.4570 500.0000
## 6 2148.1370 14589.48 3403.362 1173.9519 500.0225
## 7 1999.8200 27991.63 4707.564 1293.5547 502.2908
## 389.1814_5.311 442.19_5.313 325.1288_5.321 369.1737_5.331 344.143_5.362
## 2 12947.35 13203.137 2669.955 15131.593 3397.206
## 3 11616.72 23201.479 6323.184 5391.040 9657.898
## 4 15315.84 11578.818 5276.370 9676.561 4878.525
## 5 10024.67 10516.662 3596.335 3973.209 2631.006
## 6 11237.27 8790.464 6082.952 33698.805 4509.807
## 7 58495.24 7053.850 89544.260 6309.509 51613.250
## 413.1993_5.357 329.1602_5.369 629.3537_5.37 343.1396_5.368 200.1291_5.38
## 2 20376.252 24620.92 4531.787 14806.88 19595.50
## 3 6692.107 11565.42 4704.149 46210.96 46915.11
## 4 16209.312 13950.69 14028.584 20233.74 28769.76
## 5 6354.866 13071.86 3503.574 10672.63 22697.19
## 6 33519.004 18615.52 4097.575 24811.52 27645.81
## 7 3962.460 72749.52 6564.566 248378.98 130450.05
## 479.2283_5.383 229.0539_5.383 640.3334_5.395 662.3153_5.383 411.2021_5.398
## 2 58214.73 2134.525 1879.221 500.0201 10346.427
## 3 40937.79 121871.550 21882.246 6553.6025 13203.248
## 4 25046.62 14170.387 21452.850 5815.1357 17267.072
## 5 16035.87 57323.234 1315.734 500.0000 9837.673
## 6 21580.66 13520.754 2236.627 500.0225 14340.032
## 7 11134.73 105680.120 42352.715 9797.3790 32526.982
## 553.1926_5.396 246.1531_5.399 407.2799_5.411 621.1797_5.389 543.2074_5.421
## 2 500.0201 8996.229 1028.050 1070.4548 1085.4550
## 3 502.2117 10460.275 10389.713 502.2117 502.2117
## 4 500.2725 20256.697 1975.273 500.2725 1076.1653
## 5 500.0000 8449.521 14899.009 500.0000 500.0000
## 6 1102.4485 10757.152 2966.675 1057.1858 1269.5510
## 7 502.2908 5103.680 1206.440 502.2908 1047.4636
## 391.1968_5.431 463.0831_5.418 603.0611_5.431 467.1919_5.446 193.0353_5.465
## 2 2738.191 1340.493 500.0201 15354.949 3243.827
## 3 1827.175 2845.350 502.2117 5982.773 3619.281
## 4 2538.674 2448.247 500.2725 9843.955 3813.466
## 5 3200.716 1009.453 500.0000 15191.840 1430.522
## 6 2528.204 1852.463 500.0225 11772.958 3062.754
## 7 4559.509 3426.567 1396.4355 12405.883 5620.267
## 405.1707_5.449 407.129_5.446 474.1054_5.422 535.0762_5.453 528.2617_5.454
## 2 500.0201 500.0201 500.0201 500.0201 4031.657
## 3 502.2117 502.2117 502.2117 502.2117 2044.181
## 4 1529.2470 1062.6777 500.2725 500.2725 3067.172
## 5 500.0000 500.0000 500.0000 500.0000 1983.262
## 6 1054.0139 1142.9274 500.0225 500.0225 2681.405
## 7 1537.0580 1222.5721 1744.0751 502.2908 2752.005
## 405.118_5.456 421.0582_5.463 319.1396_5.493 305.1604_5.512 367.2123_5.512
## 2 1697.507 2708.6428 40740.530 7284.778 1740.803
## 3 1438.770 1310.8369 35223.110 3532.349 1125.647
## 4 1958.479 500.2725 36789.465 11569.745 3451.541
## 5 1247.273 1365.8150 3131.644 5746.319 1330.111
## 6 1524.505 54073.2600 12863.197 27119.117 9470.622
## 7 1623.666 1613.3344 28748.297 34464.710 5390.835
## 526.2476_5.551 379.1955_5.602 435.2232_5.611 416.1536_5.683 461.1337_5.706
## 2 18850.816 500.0201 19744.72 500.0201 3057.541
## 3 5487.000 502.2117 38704.18 2210.1304 6795.542
## 4 15333.250 1054.3843 18302.21 2178.9585 8785.196
## 5 4479.613 1295.2130 12186.93 3820.6120 10571.638
## 6 7234.536 1333.6758 13620.89 2163.3333 8601.282
## 7 24299.518 80812.1100 53611.05 2933.4980 7262.471
## 481.2073_5.68 491.1861_5.71 501.2122_5.698 583.3118_5.69 486.1199_5.689
## 2 25390.588 1320.0769 500.0201 3827.360 500.0201
## 3 11387.335 6180.4980 20087.5780 10774.948 502.2117
## 4 15662.765 1049.0277 1673.5681 7042.072 500.2725
## 5 10571.452 500.0000 1145.7780 6175.060 500.0000
## 6 6319.445 500.0225 1123.5095 15891.990 500.0225
## 7 16433.998 1133.5822 1158.9225 2899.594 502.2908
## 477.2332_5.701 500.282_5.708 445.1587_5.793 529.121_5.76 594.3642_5.768
## 2 2181.838 500.0201 3087.354 1793.982 6973.248
## 3 1590.877 502.2117 2069.001 6140.836 70584.640
## 4 1166.601 500.2725 2656.841 6818.574 12761.518
## 5 1461.699 39633.3950 2540.361 7222.016 25257.908
## 6 2061.091 500.0225 2481.289 5701.036 6704.621
## 7 16236.400 502.2908 3431.762 6164.163 5536.947
## 331.2144_5.765 448.1961_5.778 467.1506_5.777 747.3107_5.769 753.328_5.771
## 2 1633.969 500.0201 2048.356 500.0201 500.0201
## 3 2371.107 502.2117 6777.258 1497.0440 502.2117
## 4 7880.214 500.2725 10671.206 1494.2919 1102.9610
## 5 6886.719 500.0000 12089.307 500.0000 1087.0150
## 6 5070.476 1175.8634 10565.586 500.0225 500.0225
## 7 6276.514 502.2908 7449.460 502.2908 502.2908
## 461.1338_5.769 685.341_5.775 331.1763_5.775 451.125_5.775 399.1634_5.776
## 2 3057.541 500.0201 31980.71 3377.032 5757.473
## 3 8331.296 2162.3804 144166.00 9500.977 26219.334
## 4 12213.981 4053.9753 216570.28 14970.229 35131.510
## 5 11611.581 4324.6170 205911.58 15217.675 38359.668
## 6 9252.228 2833.4746 164455.47 11973.705 28834.256
## 7 10928.138 2208.0051 185423.05 11140.247 28858.064
## 387.1489_5.77 663.3572_5.772 367.1578_5.796 429.1762_5.798 407.2799_5.805
## 2 500.0201 1118.378 88616.80 16817.889 1725.492
## 3 502.2117 5551.091 34362.19 11833.716 8467.000
## 4 500.2725 2482.781 33942.82 6238.964 2241.195
## 5 500.0000 4029.835 10800.80 7995.852 13887.737
## 6 500.0225 2061.733 186158.03 4617.967 10292.009
## 7 502.2908 2043.857 27513.76 18331.111 1285.937
## 222.1134_5.811 509.2752_5.821 437.0526_5.832 511.2181_5.843 461.2388_5.842
## 2 20131.91 12520.979 7512.7690 6566.137 11973.684
## 3 32680.53 3916.246 1170.5023 12403.603 13561.204
## 4 43111.19 7785.123 500.2725 6135.029 14784.519
## 5 25701.31 10226.408 2552.1150 3394.999 9140.549
## 6 28337.33 13470.897 9565.3670 5085.640 12646.889
## 7 46498.84 7646.686 1519.2506 7645.784 23317.490
## 495.2232_5.846 529.162_5.829 345.1553_5.874 467.2645_5.878 497.2751_5.86
## 2 30918.535 500.0201 23340.36 23616.440 2610.361
## 3 6232.970 1601.7107 25768.89 3164.606 1742.850
## 4 5922.314 500.2725 35932.03 6060.458 2179.147
## 5 5351.081 500.0000 16837.64 11580.010 1959.350
## 6 2931.729 1127.5375 23477.24 12453.060 4171.294
## 7 28736.440 1354.7705 37412.69 3336.330 4375.229
## 613.3589_5.89 379.1393_5.891 463.2334_5.897 475.2184_5.932 565.3012_5.907
## 2 26207.87 1218.0563 18118.693 2698.464 13901.072
## 3 22226.75 1165.9767 6107.706 1337.656 10938.400
## 4 57371.42 500.2725 8941.656 2093.851 19063.072
## 5 19845.78 1433.1550 12609.671 500.000 7021.513
## 6 17457.36 1558.3872 20039.746 1790.844 5426.211
## 7 57183.98 1505.9944 7462.673 10889.003 5859.906
## 583.3118_5.932 491.228_5.918 567.3168_5.938 528.2633_5.962 550.2453_5.959
## 2 3671.780 3077.490 17762.637 38637.63 10067.388
## 3 14698.705 1077.964 7784.801 28933.84 10546.358
## 4 5976.672 1191.009 13694.300 50107.07 12697.360
## 5 1829.164 1246.042 7636.197 36839.25 8387.270
## 6 9048.536 3320.289 6765.653 31671.00 7620.151
## 7 9995.939 2861.871 4236.403 94076.74 24963.320
## 509.2379_5.991 443.1557_5.98 465.1373_5.985 446.2908_6.024 369.1552_5.993
## 2 3319.117 92757.78 23771.93 1971.588 8651.617
## 3 2286.994 66802.67 17166.88 10073.200 5791.534
## 4 3478.798 33966.92 10277.86 7636.633 10599.623
## 5 1776.782 45578.85 13733.12 2564.062 8412.393
## 6 3516.846 39452.70 11983.03 3319.900 9297.987
## 7 6879.712 102725.41 24294.42 11342.148 18756.162
## 345.1552_6.009 579.2778_6.011 511.2909_6.014 512.1916_6.018 624.3385_6.01
## 2 29148.08 18536.420 99607.26 500.0201 9868.496
## 3 29983.03 8165.457 40257.83 502.2117 5978.224
## 4 38178.92 8655.914 43796.23 500.2725 24226.707
## 5 15681.92 13243.390 63861.29 500.0000 6894.057
## 6 28462.55 5080.341 30582.42 500.0225 4857.556
## 7 23003.60 6157.999 24339.00 502.2908 24894.426
## 476.1913_6.018 417.2126_6.021 544.1789_6.025 496.198_6.048 257.0816_6.056
## 2 500.0201 19204.34 500.0201 500.0201 12280.1540
## 3 502.2117 17715.61 502.2117 502.2117 10617.8750
## 4 500.2725 22952.48 500.2725 500.2725 500.2725
## 5 500.0000 10533.72 500.0000 500.0000 500.0000
## 6 500.0225 18213.11 500.0225 500.0225 500.0225
## 7 502.2908 35303.63 502.2908 502.2908 502.2908
## 369.1737_6.099 437.1608_6.099 566.3329_6.114 446.2907_6.081 533.2364_6.145
## 2 66615.31 9016.866 2603.656 1971.588 55716.52
## 3 41354.35 6038.269 2093.774 8095.715 15094.43
## 4 45262.42 7218.675 6567.755 10381.021 36507.91
## 5 35725.32 5862.789 2332.630 2564.062 39470.75
## 6 130593.62 17558.717 1887.082 1796.123 43421.99
## 7 58683.89 9514.269 5535.015 5062.537 19414.73
## 465.2493_6.146 869.3936_6.15 601.2235_6.147 585.1981_6.148 523.2077_6.148
## 2 300830.66 500.0201 17970.027 18568.012 16883.537
## 3 78652.27 502.2117 5087.282 5776.009 4331.282
## 4 186971.88 500.2725 11645.340 10978.694 10122.112
## 5 202456.44 500.0000 12246.770 14130.377 12557.078
## 6 231344.11 500.0225 12881.929 13683.715 12175.300
## 7 116360.90 502.2908 7134.190 7617.415 6948.331
## 510.2524_6.169 254.6229_6.168 627.3743_6.176 457.2207_6.206 521.239_6.221
## 2 20275.67 4623.098 6618.135 500.0201 1046.2118
## 3 17112.49 3222.247 4524.073 502.2117 502.2117
## 4 44985.68 7927.392 7483.667 1003.8928 1277.0312
## 5 16190.15 3123.695 4299.785 500.0000 500.0000
## 6 16388.15 2694.713 14348.077 1160.4958 1013.5504
## 7 19043.18 2504.451 12548.565 1079.9790 1331.9034
## 393.2643_6.229 517.1295_6.231 579.0995_6.236 381.1551_6.237 193.0354_6.242
## 2 7018.804 500.0201 500.0201 2396.845 1207.0001
## 3 5735.161 502.2117 502.2117 1200.433 1142.7527
## 4 12394.640 500.2725 500.2725 1279.890 500.2725
## 5 3846.231 500.0000 500.0000 1057.665 500.0000
## 6 9533.654 500.0225 500.0225 3174.256 1533.3323
## 7 9603.908 1268.0364 502.2908 4781.780 1299.9011
## 381.1937_6.247 439.1146_6.219 501.1047_6.238 449.142_6.234 511.1128_6.237
## 2 1244.777 500.0201 500.0201 1281.9812 500.0201
## 3 1381.121 502.2117 502.2117 1067.3020 502.2117
## 4 1012.266 1008.9587 500.2725 500.2725 500.2725
## 5 1185.178 500.0000 500.0000 500.0000 500.0000
## 6 1746.768 1056.6683 500.0225 1131.5266 1022.5649
## 7 1270.590 1045.2396 1060.7882 1961.8164 502.2908
## 405.2644_6.291 573.2395_6.305 471.2417_6.297 307.1184_6.294 509.2751_6.332
## 2 1387.920 500.0201 18684.473 6102.585 21149.959
## 3 3724.927 1008.0635 18294.360 1373.262 4311.451
## 4 1505.126 500.2725 23933.836 1323.858 11959.602
## 5 2287.129 1017.5740 7752.073 2922.535 20269.045
## 6 11140.809 500.0225 6665.779 24614.324 11147.688
## 7 2388.322 5741.0110 35014.140 20257.760 3102.041
## 441.2108_6.348 419.2281_6.362 324.0682_6.363 280.0779_6.362 489.27_6.391
## 2 4303.211 11611.408 500.0201 500.0201 19273.875
## 3 3087.004 7739.516 502.2117 502.2117 15949.580
## 4 3277.203 13111.481 500.2725 500.2725 8301.648
## 5 2454.044 8878.062 500.0000 500.0000 8016.592
## 6 2949.618 4228.332 500.0225 500.0225 12656.266
## 7 5087.945 14919.423 1066.9482 502.2908 30728.766
## 448.3058_6.47 495.296_6.494 623.2016_6.501 613.1932_6.507 551.2033_6.504
## 2 17199.805 27995.438 1837.4498 1894.7000 1654.2815
## 3 7255.558 8289.567 1504.4625 1087.0563 1042.4944
## 4 13846.405 19739.857 500.2725 500.2725 500.2725
## 5 12527.042 18634.680 500.0000 500.0000 500.0000
## 6 18120.896 12107.604 1008.3889 500.0225 500.0225
## 7 3281.531 18387.506 1411.7673 1726.5679 1590.9014
## 629.2176_6.511 591.2109_6.51 493.2439_6.516 561.231_6.512 691.188_6.507
## 2 2108.0002 1857.5790 18997.682 4671.8643 1491.9589
## 3 1077.4749 1005.4697 9637.566 2719.0767 1057.6036
## 4 500.2725 500.2725 1452.618 500.2725 500.2725
## 5 500.0000 500.0000 2134.265 500.0000 1014.5690
## 6 1305.1003 1174.1191 4077.137 1140.4529 500.0225
## 7 1675.7294 1469.7109 12909.848 2969.5227 1273.0312
## 493.2869_6.516 513.2714_6.619 489.27_6.555 514.2774_6.559 446.2909_6.562
## 2 1569.7882 12710.682 22514.047 16738.121 70527.09
## 3 1346.8307 5424.427 11159.244 5405.352 23826.06
## 4 1046.6996 4672.182 8618.839 6591.541 28002.06
## 5 500.0000 3951.774 10743.843 3153.488 10707.77
## 6 500.0225 7240.870 9633.079 7264.917 24641.28
## 7 1055.0084 13957.794 22765.887 11843.554 46801.46
## 534.2499_6.614 512.2682_6.624 567.317_6.697 491.2855_6.773 407.2801_6.791
## 2 9309.471 39989.21 3238.916 4670.773 2874.274
## 3 3499.244 12448.27 9105.049 8206.888 1008.462
## 4 4768.947 16551.22 11183.833 4406.344 4265.188
## 5 2497.870 10731.49 2711.477 3076.349 1270.337
## 6 5315.484 19152.41 3992.479 2627.638 44685.710
## 7 8661.400 39859.50 12001.492 11399.526 1092.445
## 507.2231_6.98 505.2452_7.002 267.1235_7.063 437.18_7.078 573.2393_7.26
## 2 4173.197 500.0201 34378.414 1187.5945 500.0201
## 3 25650.576 502.2117 23522.305 1160.8503 502.2117
## 4 22368.453 1112.6277 9432.807 500.2725 500.2725
## 5 32762.258 500.0000 87857.734 500.0000 1037.9590
## 6 24798.738 500.0225 58489.620 6081.6685 500.0225
## 7 19402.658 502.2908 53023.780 1701.7600 3339.9758
## 673.2558_7.365 519.223_7.484 557.2465_7.479 517.2413_8.045 449.254_8.044
## 2 500.0201 1176.9064 1107.4280 20837.479 116360.600
## 3 502.2117 502.2117 1055.2023 1358.101 6277.505
## 4 500.2725 1026.7890 500.2725 12336.555 62160.650
## 5 500.0000 1087.8320 500.0000 14514.675 76166.440
## 6 500.0225 1020.6257 500.0225 20214.268 113360.130
## 7 5379.2450 502.2908 2644.6428 1239.258 3941.558Find connections
connection <- find_connections(data = data_notame,
features = features,
corr_thresh = 0.9,
rt_window = 1/60,
name_col = "mz_rt",
mz_col = "mz",
rt_col = "rt")## [1] 100
## [1] 200
## [1] 300
## [1] 400
## [1] 500
## [1] 600
## [1] 700
## [1] 800
## [1] 900
## [1] 1000
## [1] 1100
## [1] 1200
## [1] 1300
## [1] 1400## x y cor rt_diff mz_diff
## 1 236.065_0.603 168.0777_0.606 0.9761211 0.003 -67.9873
## 2 275.056_0.654 275.0569_0.663 0.9311993 0.009 0.0009
## 3 265.0732_0.64 321.0625_0.656 0.9808950 0.016 55.9893
## 4 277.0852_0.658 321.0625_0.656 0.9038537 -0.002 43.9773
## 5 321.0625_0.656 373.0567_0.663 0.9401514 0.007 51.9942
## 6 151.0066_0.66 351.0199_0.667 0.9261984 0.007 200.0133Clustering
## 153 components found
##
## Component 100 / 153
## 67 components found
##
## 29 components found
##
## 9 components found
##
## 1 components found# assign a cluster ID to all features. Clusters are named after feature with highest median peak height
features_clustered <- assign_cluster_id(data_notame, clusters, features, name_col = "mz_rt")
# lets see how many features are removed when we only keep one feature per cluster
pulled <- pull_clusters(data_notame, features_clustered, name_col = "mz_rt")
cluster_data <- pulled$cdata
cluster_features <- pulled$cfeatures
# how much did we trim our data down by?
nrow(omicsdata) - nrow(cluster_features)## [1] 486Reduce dataset based on clustering
# transpose the full dataset back to wide so that it is more similar to the notame dataset
imp_meta_omics_wide <- imp_meta_omics %>%
dplyr::select(-"row_ID") %>%
pivot_wider(names_from = mz_rt,
values_from = peak_height)
# list of reduced features
clusternames <- cluster_features$mz_rt
# select only the features are in the reduced list
imp_clust <- imp_meta_omics_wide[,c(names(imp_meta_omics_wide) %in% clusternames)]
# bind back sample names
imp_clust <- cbind(imp_meta_omics_wide[1], imp_clust)
tibble(imp_clust)## # A tibble: 56 × 924
## sample_ID `168.0777_0.606` `154.0621_0.609` `124.0072_0.616` `193.0351_0.617`
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 6111_U4_… 6707. 304458. 63059. 110661.
## 2 6101_U2_… 81842. 112404. 22064. 50736.
## 3 6102_U1_… 29358. 168966. 56237. 54823.
## 4 6110_U2_… 19074. 67706. 22557. 27334.
## 5 6105_U4_… 59856. 208993. 56522. 51905.
## 6 6103_U4_… 90745. 92892. 4167. 81048.
## 7 6105_U1_… 36894. 248975. 63102. 70254.
## 8 6104_U1_… 29693. 322896 51042. 56236.
## 9 6113_U4_… 110885. 155423. 44750. 73910.
## 10 6105_U2_… 64532. 262397. 47441. 94744
## # ℹ 46 more rows
## # ℹ 919 more variables: `239.1144_0.622` <dbl>, `195.051_0.627` <dbl>,
## # `215.0326_0.629` <dbl>, `219.0457_0.629` <dbl>, `217.0486_0.63` <dbl>,
## # `245.0433_0.63` <dbl>, `165.0405_0.634` <dbl>, `187.0383_0.634` <dbl>,
## # `235.057_0.641` <dbl>, `135.0295_0.64` <dbl>, `277.0852_0.658` <dbl>,
## # `179.0556_0.643` <dbl>, `149.0089_0.649` <dbl>, `547.1106_0.67` <dbl>,
## # `149.0452_0.654` <dbl>, `262.0364_0.654` <dbl>, `321.0625_0.656` <dbl>, …New mz vs rt plot
Let’s see how our clustered data looks now compared to the original
# plot new rt vs mz scatterplot post-clustering
(plot_mzvsrt_postcluster <- cluster_features %>%
ggplot(aes(x = rt,
y = mz)) +
geom_point() +
theme_minimal() +
labs(x = "Retention time, min",
y = "m/z, neutral",
title = "mz across RT for all features after clustering"))
# plot both scatterplots to compare with and without notame clustering
(scatterplots <- ggarrange(plot_mzvsrt,
plot_mzvsrt_postcluster,
nrow = 2))
There are still some features that line up, but the clustered data looks much better.
Wrangle new data
Visualize untransformed data
More wrangling
# change meta data columns to character so that I can change NAs from QCs to "QC"
imp_metabind_clust <- imp_metabind_clust %>%
mutate_at(c("Subject",
"Period",
"Intervention",
"pre_post",
"sequence",
"Intervention_week",
"Sex",
"Age",
"BMI"),
as.character)
# replace NAs in metadata columns for QCs
imp_metabind_clust[is.na(imp_metabind_clust)] <- "QC"
# unite pre_post column with intervention column to create pre_intervention column
imp_metabind_clust <- imp_metabind_clust %>%
unite(col = "pre_post_intervention",
c("pre_post","Intervention"),
sep = "_",
remove = FALSE)
# long df
imp_metabind_clust_tidy <- imp_metabind_clust %>%
pivot_longer(cols = 12:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund")
# structure check
str(imp_metabind_clust_tidy)## tibble [51,688 × 13] (S3: tbl_df/tbl/data.frame)
## $ sample_ID : chr [1:51688] "6101_U1_C18NEG_59" "6101_U1_C18NEG_59" "6101_U1_C18NEG_59" "6101_U1_C18NEG_59" ...
## $ Subject : chr [1:51688] "6101" "6101" "6101" "6101" ...
## $ Period : chr [1:51688] "U1" "U1" "U1" "U1" ...
## $ pre_post_intervention: chr [1:51688] "pre_Red" "pre_Red" "pre_Red" "pre_Red" ...
## $ Intervention : chr [1:51688] "Red" "Red" "Red" "Red" ...
## $ pre_post : chr [1:51688] "pre" "pre" "pre" "pre" ...
## $ sequence : chr [1:51688] "R_Y" "R_Y" "R_Y" "R_Y" ...
## $ Intervention_week : chr [1:51688] "2" "2" "2" "2" ...
## $ Sex : chr [1:51688] "F" "F" "F" "F" ...
## $ Age : chr [1:51688] "58" "58" "58" "58" ...
## $ BMI : chr [1:51688] "31.0556029483653" "31.0556029483653" "31.0556029483653" "31.0556029483653" ...
## $ mz_rt : chr [1:51688] "168.0777_0.606" "154.0621_0.609" "124.0072_0.616" "193.0351_0.617" ...
## $ rel_abund : num [1:51688] 152309 169447 29742 66817 18535 ...Boxplot
imp_metabind_clust_tidy %>%
ggplot(aes(x = sample_ID, y = rel_abund, color = Intervention)) +
geom_boxplot(alpha = 0.6) +
scale_color_manual(values = c("light grey", "tomato1", "gold")) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "LC-MS (-) Feature Abundances by Sample",
subtitle = "Unscaled data",
y = "Relative abundance")
Will need to log transform in order to normalize and actually see the data
Log2 transform
imp_metabind_clust_tidy_log2 <- imp_metabind_clust_tidy %>%
mutate(rel_abund_log2 = log2(rel_abund))Boxplot
(bp_data_quality <- imp_metabind_clust_tidy_log2 %>%
ggplot(aes(x = sample_ID, y = rel_abund_log2, fill = Intervention)) +
geom_boxplot(alpha = 0.6) +
scale_fill_manual(values = c("light grey", "tomato1", "gold")) +
theme_minimal() +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "LC-MS (-) Feature Abundances by Sample",
subtitle = "Log2 transformed data",
y = "Relative abundance"))
Much better! QCs look good.
PCAs
With QCS
Wrangle
# go back to wide data
imp_metabind_clust_log2 <- imp_metabind_clust_tidy_log2 %>%
dplyr::select(!rel_abund) %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2)
PCA.imp_metabind_clust_log2 <- PCA(imp_metabind_clust_log2, # wide data
quali.sup = 1:11, # remove qualitative variables
graph = FALSE, # don't graph
scale.unit = FALSE) # don't scale, already transformed data
# PCA summary
kable(summary(PCA.imp_metabind_clust_log2))##
## Call:
## PCA(X = imp_metabind_clust_log2, scale.unit = FALSE, quali.sup = 1:11,
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 683.392 199.020 123.670 102.071 94.088 74.721 63.366
## % of var. 36.934 10.756 6.684 5.516 5.085 4.038 3.425
## Cumulative % of var. 36.934 47.690 54.374 59.891 64.976 69.014 72.439
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 52.088 41.754 36.438 30.649 29.881 26.059 21.995
## % of var. 2.815 2.257 1.969 1.656 1.615 1.408 1.189
## Cumulative % of var. 75.254 77.510 79.480 81.136 82.751 84.159 85.348
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 19.653 16.815 16.386 15.053 14.039 12.578 12.133
## % of var. 1.062 0.909 0.886 0.814 0.759 0.680 0.656
## Cumulative % of var. 86.410 87.319 88.205 89.018 89.777 90.457 91.112
## Dim.22 Dim.23 Dim.24 Dim.25 Dim.26 Dim.27 Dim.28
## Variance 11.907 10.517 10.337 9.384 9.071 8.277 7.809
## % of var. 0.644 0.568 0.559 0.507 0.490 0.447 0.422
## Cumulative % of var. 91.756 92.324 92.883 93.390 93.880 94.328 94.750
## Dim.29 Dim.30 Dim.31 Dim.32 Dim.33 Dim.34 Dim.35
## Variance 7.721 7.036 6.629 6.461 6.110 5.745 5.622
## % of var. 0.417 0.380 0.358 0.349 0.330 0.311 0.304
## Cumulative % of var. 95.167 95.547 95.906 96.255 96.585 96.895 97.199
## Dim.36 Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 5.347 4.991 4.888 4.338 4.188 3.996 3.826
## % of var. 0.289 0.270 0.264 0.234 0.226 0.216 0.207
## Cumulative % of var. 97.488 97.758 98.022 98.257 98.483 98.699 98.906
## Dim.43 Dim.44 Dim.45 Dim.46 Dim.47 Dim.48 Dim.49
## Variance 3.569 3.347 3.000 2.842 2.806 2.378 0.425
## % of var. 0.193 0.181 0.162 0.154 0.152 0.129 0.023
## Cumulative % of var. 99.099 99.279 99.442 99.595 99.747 99.875 99.898
## Dim.50 Dim.51 Dim.52 Dim.53 Dim.54 Dim.55
## Variance 0.392 0.345 0.327 0.289 0.270 0.258
## % of var. 0.021 0.019 0.018 0.016 0.015 0.014
## Cumulative % of var. 99.919 99.938 99.956 99.971 99.986 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 33.859 | -19.706 1.015 0.339 | -10.370 0.965 0.094
## 2 | 33.433 | -21.457 1.203 0.412 | -13.706 1.686 0.168
## 3 | 38.898 | -7.407 0.143 0.036 | 6.261 0.352 0.026
## 4 | 39.035 | -20.230 1.069 0.269 | -11.999 1.292 0.094
## 5 | 39.371 | -17.492 0.800 0.197 | -8.848 0.702 0.051
## 6 | 71.848 | 60.047 9.422 0.698 | -27.379 6.726 0.145
## 7 | 36.871 | -17.102 0.764 0.215 | -5.206 0.243 0.020
## 8 | 36.948 | -10.259 0.275 0.077 | -0.526 0.002 0.000
## 9 | 29.525 | -12.729 0.423 0.186 | 0.168 0.000 0.000
## 10 | 35.950 | -13.189 0.455 0.135 | -1.858 0.031 0.003
## Dim.3 ctr cos2
## 1 | -4.291 0.266 0.016 |
## 2 | -4.629 0.309 0.019 |
## 3 | -5.284 0.403 0.018 |
## 4 | -4.063 0.238 0.011 |
## 5 | -1.953 0.055 0.002 |
## 6 | -10.466 1.582 0.021 |
## 7 | -3.790 0.207 0.011 |
## 8 | -10.837 1.696 0.086 |
## 9 | 3.866 0.216 0.017 |
## 10 | -7.047 0.717 0.038 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 168.0777_0.606 | -0.172 0.004 0.012 | -0.093 0.004 0.004 | 0.522 0.220
## 154.0621_0.609 | -0.079 0.001 0.010 | -0.131 0.009 0.027 | 0.092 0.007
## 124.0072_0.616 | -0.056 0.000 0.002 | -0.316 0.050 0.070 | 0.336 0.091
## 193.0351_0.617 | 0.162 0.004 0.105 | 0.085 0.004 0.029 | -0.101 0.008
## 239.1144_0.622 | -0.152 0.003 0.016 | -0.081 0.003 0.004 | 0.285 0.066
## 195.051_0.627 | -0.053 0.000 0.016 | 0.100 0.005 0.058 | -0.114 0.010
## 215.0326_0.629 | -0.138 0.003 0.100 | 0.073 0.003 0.028 | -0.025 0.000
## 219.0457_0.629 | -0.165 0.004 0.038 | 0.184 0.017 0.048 | 0.050 0.002
## 217.0486_0.63 | -0.104 0.002 0.016 | 0.200 0.020 0.058 | 0.035 0.001
## 245.0433_0.63 | -0.100 0.001 0.024 | 0.149 0.011 0.052 | 0.051 0.002
## cos2
## 168.0777_0.606 0.115 |
## 154.0621_0.609 0.013 |
## 124.0072_0.616 0.079 |
## 193.0351_0.617 0.041 |
## 239.1144_0.622 0.055 |
## 195.051_0.627 0.074 |
## 215.0326_0.629 0.003 |
## 219.0457_0.629 0.003 |
## 217.0486_0.63 0.002 |
## 245.0433_0.63 0.006 |
##
## Supplementary categories (the 10 first)
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test
## 6101_U1_C18NEG_59 | 33.859 | -19.706 0.339 -0.754 | -10.370 0.094 -0.735
## 6101_U2_C18NEG_30 | 37.365 | -15.616 0.175 -0.597 | 0.084 0.000 0.006
## 6101_U3_C18NEG_29 | 34.060 | -20.953 0.378 -0.802 | -9.637 0.080 -0.683
## 6101_U4_C18NEG_14 | 35.550 | -19.295 0.295 -0.738 | -8.036 0.051 -0.570
## 6102_U1_C18NEG_26 | 33.433 | -21.457 0.412 -0.821 | -13.706 0.168 -0.972
## 6102_U2_C18NEG_16 | 33.451 | -15.025 0.202 -0.575 | 0.911 0.001 0.065
## 6102_U3_C18NEG_48 | 38.088 | -16.350 0.184 -0.625 | -1.375 0.001 -0.097
## 6102_U4_C18NEG_50 | 31.034 | -14.002 0.204 -0.536 | -0.185 0.000 -0.013
## 6103_U1_C18NEG_21 | 38.898 | -7.407 0.036 -0.283 | 6.261 0.026 0.444
## 6103_U2_C18NEG_60 | 42.492 | -11.051 0.068 -0.423 | 6.634 0.024 0.470
## Dim.3 cos2 v.test
## 6101_U1_C18NEG_59 | -4.291 0.016 -0.386 |
## 6101_U2_C18NEG_30 | 19.331 0.268 1.738 |
## 6101_U3_C18NEG_29 | -1.538 0.002 -0.138 |
## 6101_U4_C18NEG_14 | -6.816 0.037 -0.613 |
## 6102_U1_C18NEG_26 | -4.629 0.019 -0.416 |
## 6102_U2_C18NEG_16 | 16.983 0.258 1.527 |
## 6102_U3_C18NEG_48 | -4.878 0.016 -0.439 |
## 6102_U4_C18NEG_50 | -8.331 0.072 -0.749 |
## 6103_U1_C18NEG_21 | -5.284 0.018 -0.475 |
## 6103_U2_C18NEG_60 | 17.452 0.169 1.569 || Dist | Dim.1 | cos2 | v.test | Dim.2 | cos2 | v.test | Dim.3 | cos2 | v.test | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6101_U1_C18NEG_59 | | | 33.859 | | | -19.706 | 0.339 | -0.754 | | | -10.370 | 0.094 | -0.735 | | | -4.291 | 0.016 | -0.386 | | |
| 6101_U2_C18NEG_30 | | | 37.365 | | | -15.616 | 0.175 | -0.597 | | | 0.084 | 0.000 | 0.006 | | | 19.331 | 0.268 | 1.738 | | |
| 6101_U3_C18NEG_29 | | | 34.060 | | | -20.953 | 0.378 | -0.802 | | | -9.637 | 0.080 | -0.683 | | | -1.538 | 0.002 | -0.138 | | |
| 6101_U4_C18NEG_14 | | | 35.550 | | | -19.295 | 0.295 | -0.738 | | | -8.036 | 0.051 | -0.570 | | | -6.816 | 0.037 | -0.613 | | |
| 6102_U1_C18NEG_26 | | | 33.433 | | | -21.457 | 0.412 | -0.821 | | | -13.706 | 0.168 | -0.972 | | | -4.629 | 0.019 | -0.416 | | |
| 6102_U2_C18NEG_16 | | | 33.451 | | | -15.025 | 0.202 | -0.575 | | | 0.911 | 0.001 | 0.065 | | | 16.983 | 0.258 | 1.527 | | |
| 6102_U3_C18NEG_48 | | | 38.088 | | | -16.350 | 0.184 | -0.625 | | | -1.375 | 0.001 | -0.097 | | | -4.878 | 0.016 | -0.439 | | |
| 6102_U4_C18NEG_50 | | | 31.034 | | | -14.002 | 0.204 | -0.536 | | | -0.185 | 0.000 | -0.013 | | | -8.331 | 0.072 | -0.749 | | |
| 6103_U1_C18NEG_21 | | | 38.898 | | | -7.407 | 0.036 | -0.283 | | | 6.261 | 0.026 | 0.444 | | | -5.284 | 0.018 | -0.475 | | |
| 6103_U2_C18NEG_60 | | | 42.492 | | | -11.051 | 0.068 | -0.423 | | | 6.634 | 0.024 | 0.470 | | | 17.452 | 0.169 | 1.569 | | |
# pull PC coordinates into df
PC_coord_QC_log2 <- as.data.frame(PCA.imp_metabind_clust_log2$ind$coord)
# bind back metadata from cols 1-10
PC_coord_QC_log2 <- bind_cols(imp_metabind_clust_log2[,1:11], PC_coord_QC_log2)
# grab some variance explained
importance_QC <- PCA.imp_metabind_clust_log2$eig
# set variance explained with PC1, round to 2 digits
PC1_withQC <- round(importance_QC[1,2], 2)
# set variance explained with PC2, round to 2 digits
PC2_withQC <- round(importance_QC[2,2], 2)Plots
Using FactoExtra package
| eigenvalue | variance.percent | cumulative.variance.percent | |
|---|---|---|---|
| Dim.1 | 683.3918481 | 36.9342082 | 36.93421 |
| Dim.2 | 199.0196047 | 10.7561007 | 47.69031 |
| Dim.3 | 123.6695634 | 6.6837751 | 54.37408 |
| Dim.4 | 102.0708619 | 5.5164639 | 59.89055 |
| Dim.5 | 94.0878635 | 5.0850193 | 64.97557 |
| Dim.6 | 74.7207523 | 4.0383154 | 69.01388 |
| Dim.7 | 63.3657151 | 3.4246275 | 72.43851 |
| Dim.8 | 52.0876271 | 2.8150984 | 75.25361 |
| Dim.9 | 41.7541278 | 2.2566199 | 77.51023 |
| Dim.10 | 36.4384195 | 1.9693302 | 79.47956 |
| Dim.11 | 30.6488590 | 1.6564309 | 81.13599 |
| Dim.12 | 29.8811065 | 1.6149373 | 82.75093 |
| Dim.13 | 26.0587124 | 1.4083544 | 84.15928 |
| Dim.14 | 21.9948367 | 1.1887205 | 85.34800 |
| Dim.15 | 19.6529538 | 1.0621524 | 86.41015 |
| Dim.16 | 16.8154251 | 0.9087969 | 87.31895 |
| Dim.17 | 16.3863365 | 0.8856066 | 88.20456 |
| Dim.18 | 15.0526980 | 0.8135296 | 89.01809 |
| Dim.19 | 14.0390340 | 0.7587457 | 89.77683 |
| Dim.20 | 12.5781503 | 0.6797916 | 90.45662 |
| Dim.21 | 12.1332234 | 0.6557453 | 91.11237 |
| Dim.22 | 11.9071599 | 0.6435276 | 91.75590 |
| Dim.23 | 10.5169969 | 0.5683956 | 92.32429 |
| Dim.24 | 10.3365270 | 0.5586421 | 92.88294 |
| Dim.25 | 9.3840090 | 0.5071628 | 93.39010 |
| Dim.26 | 9.0714465 | 0.4902703 | 93.88037 |
| Dim.27 | 8.2774007 | 0.4473557 | 94.32772 |
| Dim.28 | 7.8088541 | 0.4220329 | 94.74976 |
| Dim.29 | 7.7209716 | 0.4172833 | 95.16704 |
| Dim.30 | 7.0356148 | 0.3802428 | 95.54728 |
| Dim.31 | 6.6288269 | 0.3582578 | 95.90554 |
| Dim.32 | 6.4605273 | 0.3491620 | 96.25470 |
| Dim.33 | 6.1102163 | 0.3302293 | 96.58493 |
| Dim.34 | 5.7451875 | 0.3105011 | 96.89543 |
| Dim.35 | 5.6221854 | 0.3038534 | 97.19929 |
| Dim.36 | 5.3466651 | 0.2889628 | 97.48825 |
| Dim.37 | 4.9905238 | 0.2697150 | 97.75796 |
| Dim.38 | 4.8883642 | 0.2641938 | 98.02216 |
| Dim.39 | 4.3382805 | 0.2344642 | 98.25662 |
| Dim.40 | 4.1875363 | 0.2263172 | 98.48294 |
| Dim.41 | 3.9960660 | 0.2159691 | 98.69891 |
| Dim.42 | 3.8258030 | 0.2067672 | 98.90568 |
| Dim.43 | 3.5689283 | 0.1928843 | 99.09856 |
| Dim.44 | 3.3473644 | 0.1809098 | 99.27947 |
| Dim.45 | 2.9998579 | 0.1621286 | 99.44160 |
| Dim.46 | 2.8418558 | 0.1535893 | 99.59519 |
| Dim.47 | 2.8055619 | 0.1516278 | 99.74682 |
| Dim.48 | 2.3780328 | 0.1285218 | 99.87534 |
| Dim.49 | 0.4247864 | 0.0229578 | 99.89830 |
| Dim.50 | 0.3918083 | 0.0211754 | 99.91947 |
| Dim.51 | 0.3453639 | 0.0186653 | 99.93814 |
| Dim.52 | 0.3273038 | 0.0176893 | 99.95583 |
| Dim.53 | 0.2894124 | 0.0156414 | 99.97147 |
| Dim.54 | 0.2704455 | 0.0146163 | 99.98608 |
| Dim.55 | 0.2575039 | 0.0139169 | 100.00000 |
Manual scores plot
# manual scores plot
(PCA_withQCs <- PC_coord_QC_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = factor(Intervention, levels = c("Yellow", "Red", "QC")))) +
geom_point(shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("gold", "tomato1", "light grey")) +
scale_color_manual(values = "black") +
theme_minimal() +
coord_fixed(PC2_withQC/PC1_withQC) +
labs(x = glue::glue("PC1: {PC1_withQC}%"),
y = glue::glue("PC2: {PC2_withQC}%"),
fill = "Group",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data"))
There seems to be outliers here. We’ll keep investigating. Let’s see how the data looks without the QCs.
Without QCs
Wrangle
imp_metabind_clust_log2_noQCs <- imp_metabind_clust_log2 %>%
filter(Intervention != "QC")
PCA.imp_metabind_clust_log2_noQCs <- PCA(imp_metabind_clust_log2_noQCs, # wide data
quali.sup=1:11, # remove qualitative variables
graph=FALSE, # don't graph
scale.unit=FALSE) # don't scale, we already did this
# look at summary
kable(summary(PCA.imp_metabind_clust_log2_noQCs))##
## Call:
## PCA(X = imp_metabind_clust_log2_noQCs, scale.unit = FALSE, quali.sup = 1:11,
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 539.822 169.381 140.275 110.123 91.704 76.898 66.493
## % of var. 31.065 9.747 8.072 6.337 5.277 4.425 3.826
## Cumulative % of var. 31.065 40.812 48.884 55.221 60.498 64.923 68.750
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 49.220 43.349 38.721 35.181 30.920 25.958 23.763
## % of var. 2.832 2.495 2.228 2.025 1.779 1.494 1.367
## Cumulative % of var. 71.582 74.077 76.305 78.330 80.109 81.603 82.970
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 20.675 19.277 18.702 16.843 14.911 14.195 14.087
## % of var. 1.190 1.109 1.076 0.969 0.858 0.817 0.811
## Cumulative % of var. 84.160 85.269 86.345 87.315 88.173 88.990 89.800
## Dim.22 Dim.23 Dim.24 Dim.25 Dim.26 Dim.27 Dim.28
## Variance 12.382 12.109 11.608 10.913 9.728 9.273 9.058
## % of var. 0.713 0.697 0.668 0.628 0.560 0.534 0.521
## Cumulative % of var. 90.513 91.210 91.878 92.506 93.065 93.599 94.120
## Dim.29 Dim.30 Dim.31 Dim.32 Dim.33 Dim.34 Dim.35
## Variance 8.460 7.734 7.555 7.122 6.770 6.565 6.288
## % of var. 0.487 0.445 0.435 0.410 0.390 0.378 0.362
## Cumulative % of var. 94.607 95.052 95.487 95.897 96.286 96.664 97.026
## Dim.36 Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 5.829 5.787 5.065 4.885 4.665 4.468 4.159
## % of var. 0.335 0.333 0.291 0.281 0.268 0.257 0.239
## Cumulative % of var. 97.362 97.695 97.986 98.267 98.536 98.793 99.032
## Dim.43 Dim.44 Dim.45 Dim.46 Dim.47
## Variance 3.898 3.543 3.332 3.261 2.788
## % of var. 0.224 0.204 0.192 0.188 0.160
## Cumulative % of var. 99.256 99.460 99.652 99.840 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 29.956 | -10.598 0.433 0.125 | -10.477 1.350 0.122
## 2 | 28.555 | -10.685 0.441 0.140 | -11.534 1.636 0.163
## 3 | 38.642 | -3.423 0.045 0.008 | 7.442 0.681 0.037
## 4 | 35.702 | -11.267 0.490 0.100 | -14.830 2.705 0.173
## 5 | 36.649 | -9.241 0.330 0.064 | -10.296 1.304 0.079
## 6 | 76.447 | 71.291 19.615 0.870 | -1.760 0.038 0.001
## 7 | 34.148 | -9.610 0.356 0.079 | -6.667 0.547 0.038
## 8 | 35.420 | -3.150 0.038 0.008 | 6.759 0.562 0.036
## 9 | 27.862 | -7.620 0.224 0.075 | -3.192 0.125 0.013
## 10 | 33.850 | -5.868 0.133 0.030 | 3.666 0.165 0.012
## Dim.3 ctr cos2
## 1 | -8.581 1.094 0.082 |
## 2 | -8.078 0.969 0.080 |
## 3 | -4.185 0.260 0.012 |
## 4 | -11.152 1.847 0.098 |
## 5 | -6.240 0.578 0.029 |
## 6 | -8.485 1.069 0.012 |
## 7 | -8.005 0.952 0.055 |
## 8 | -7.665 0.873 0.047 |
## 9 | 2.120 0.067 0.006 |
## 10 | -4.304 0.275 0.016 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 168.0777_0.606 | -0.341 0.022 0.043 | -0.747 0.329 0.204 | 0.134 0.013
## 154.0621_0.609 | -0.097 0.002 0.013 | -0.276 0.045 0.103 | -0.013 0.000
## 124.0072_0.616 | -0.101 0.002 0.006 | -0.709 0.296 0.306 | 0.047 0.002
## 193.0351_0.617 | 0.181 0.006 0.113 | 0.284 0.048 0.278 | 0.046 0.002
## 239.1144_0.622 | -0.200 0.007 0.023 | -0.414 0.101 0.101 | 0.083 0.005
## 195.051_0.627 | -0.082 0.001 0.033 | 0.135 0.011 0.090 | -0.075 0.004
## 215.0326_0.629 | -0.174 0.006 0.136 | 0.022 0.000 0.002 | -0.026 0.000
## 219.0457_0.629 | -0.324 0.019 0.128 | -0.095 0.005 0.011 | -0.080 0.005
## 217.0486_0.63 | -0.281 0.015 0.102 | -0.061 0.002 0.005 | -0.077 0.004
## 245.0433_0.63 | -0.228 0.010 0.107 | -0.051 0.002 0.005 | -0.029 0.001
## cos2
## 168.0777_0.606 0.007 |
## 154.0621_0.609 0.000 |
## 124.0072_0.616 0.001 |
## 193.0351_0.617 0.007 |
## 239.1144_0.622 0.004 |
## 195.051_0.627 0.028 |
## 215.0326_0.629 0.003 |
## 219.0457_0.629 0.008 |
## 217.0486_0.63 0.008 |
## 245.0433_0.63 0.002 |
##
## Supplementary categories (the 10 first)
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test
## 6101_U1_C18NEG_59 | 29.956 | -10.598 0.125 -0.456 | -10.477 0.122 -0.805
## 6101_U2_C18NEG_30 | 35.342 | -9.827 0.077 -0.423 | -5.188 0.022 -0.399
## 6101_U3_C18NEG_29 | 29.987 | -12.056 0.162 -0.519 | -10.972 0.134 -0.843
## 6101_U4_C18NEG_14 | 32.060 | -10.748 0.112 -0.463 | -7.377 0.053 -0.567
## 6102_U1_C18NEG_26 | 28.555 | -10.685 0.140 -0.460 | -11.534 0.163 -0.886
## 6102_U2_C18NEG_16 | 31.178 | -9.010 0.084 -0.388 | -1.759 0.003 -0.135
## 6102_U3_C18NEG_48 | 35.927 | -10.109 0.079 -0.435 | -1.343 0.001 -0.103
## 6102_U4_C18NEG_50 | 28.780 | -7.787 0.073 -0.335 | 2.039 0.005 0.157
## 6103_U1_C18NEG_21 | 38.642 | -3.423 0.008 -0.147 | 7.442 0.037 0.572
## 6103_U2_C18NEG_60 | 41.607 | -6.706 0.026 -0.289 | 3.723 0.008 0.286
## Dim.3 cos2 v.test
## 6101_U1_C18NEG_59 | -8.581 0.082 -0.725 |
## 6101_U2_C18NEG_30 | 19.468 0.303 1.644 |
## 6101_U3_C18NEG_29 | -5.910 0.039 -0.499 |
## 6101_U4_C18NEG_14 | -10.218 0.102 -0.863 |
## 6102_U1_C18NEG_26 | -8.078 0.080 -0.682 |
## 6102_U2_C18NEG_16 | 19.199 0.379 1.621 |
## 6102_U3_C18NEG_48 | -5.800 0.026 -0.490 |
## 6102_U4_C18NEG_50 | -8.232 0.082 -0.695 |
## 6103_U1_C18NEG_21 | -4.185 0.012 -0.353 |
## 6103_U2_C18NEG_60 | 20.708 0.248 1.748 || Dist | Dim.1 | cos2 | v.test | Dim.2 | cos2 | v.test | Dim.3 | cos2 | v.test | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6101_U1_C18NEG_59 | | | 29.956 | | | -10.598 | 0.125 | -0.456 | | | -10.477 | 0.122 | -0.805 | | | -8.581 | 0.082 | -0.725 | | |
| 6101_U2_C18NEG_30 | | | 35.342 | | | -9.827 | 0.077 | -0.423 | | | -5.188 | 0.022 | -0.399 | | | 19.468 | 0.303 | 1.644 | | |
| 6101_U3_C18NEG_29 | | | 29.987 | | | -12.056 | 0.162 | -0.519 | | | -10.972 | 0.134 | -0.843 | | | -5.910 | 0.039 | -0.499 | | |
| 6101_U4_C18NEG_14 | | | 32.060 | | | -10.748 | 0.112 | -0.463 | | | -7.377 | 0.053 | -0.567 | | | -10.218 | 0.102 | -0.863 | | |
| 6102_U1_C18NEG_26 | | | 28.555 | | | -10.685 | 0.140 | -0.460 | | | -11.534 | 0.163 | -0.886 | | | -8.078 | 0.080 | -0.682 | | |
| 6102_U2_C18NEG_16 | | | 31.178 | | | -9.010 | 0.084 | -0.388 | | | -1.759 | 0.003 | -0.135 | | | 19.199 | 0.379 | 1.621 | | |
| 6102_U3_C18NEG_48 | | | 35.927 | | | -10.109 | 0.079 | -0.435 | | | -1.343 | 0.001 | -0.103 | | | -5.800 | 0.026 | -0.490 | | |
| 6102_U4_C18NEG_50 | | | 28.780 | | | -7.787 | 0.073 | -0.335 | | | 2.039 | 0.005 | 0.157 | | | -8.232 | 0.082 | -0.695 | | |
| 6103_U1_C18NEG_21 | | | 38.642 | | | -3.423 | 0.008 | -0.147 | | | 7.442 | 0.037 | 0.572 | | | -4.185 | 0.012 | -0.353 | | |
| 6103_U2_C18NEG_60 | | | 41.607 | | | -6.706 | 0.026 | -0.289 | | | 3.723 | 0.008 | 0.286 | | | 20.708 | 0.248 | 1.748 | | |
# pull PC coordinates into df
PC_coord_noQCs_log2 <- as.data.frame(PCA.imp_metabind_clust_log2_noQCs$ind$coord)
# bind back metadata from cols 1-10
PC_coord_noQCs_log2 <- bind_cols(imp_metabind_clust_log2_noQCs[,1:11], PC_coord_noQCs_log2)
# grab some variance explained
importance_noQC <- PCA.imp_metabind_clust_log2_noQCs$eig
# set variance explained with PC1, round to 2 digits
PC1_noQC <- round(importance_noQC[1,2], 2)
# set variance explained with PC2, round to 2 digits
PC2_noQC <- round(importance_noQC[2,2], 2)Plots
Using FactoExtra
Manual scores plot
(PCA_withoutQCs <- PC_coord_noQCs_log2 %>%
ggplot(aes(x = Dim.1,
y = Dim.2,
fill = Intervention,
text = sample_ID)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("gold", "tomato1")) +
scale_color_manual(values = "black") +
theme_minimal() +
coord_fixed(PC2_noQC/PC1_noQC) +
labs(x = glue::glue("PC1: {PC1_noQC}%"),
y = glue::glue("PC2: {PC2_noQC}%"),
fill = "Intervention",
title = "Principal Components Analysis Scores Plot",
subtitle = "Log2 transformed data, without QCs"))
Interactive plot shows us that all of those visual outliers are subject 6106. Let’s see what the data looks like when we remove this outlier.
Removal of outlier
With QCs
Wrangle
# go back to wide data
imp_no6106_log2 <- imp_metabind_clust_tidy_log2 %>%
dplyr::select(!rel_abund) %>%
filter(Subject != 6106) %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2)
PCA.imp_no6106_log2 <- PCA(imp_no6106_log2, # wide data
quali.sup = 1:11, # remove qualitative variables
graph = FALSE, # don't graph
scale.unit = FALSE) # don't scale, already transformed data
# PCA summary
kable(summary(PCA.imp_no6106_log2))##
## Call:
## PCA(X = imp_no6106_log2, scale.unit = FALSE, quali.sup = 1:11,
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 490.928 136.097 119.342 100.676 81.704 69.462 57.867
## % of var. 32.241 8.938 7.838 6.612 5.366 4.562 3.800
## Cumulative % of var. 32.241 41.179 49.016 55.628 60.994 65.556 69.356
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 46.064 36.930 32.740 32.399 27.047 23.881 21.976
## % of var. 3.025 2.425 2.150 2.128 1.776 1.568 1.443
## Cumulative % of var. 72.381 74.806 76.957 79.084 80.861 82.429 83.872
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 18.147 17.492 16.217 13.718 13.260 12.725 11.859
## % of var. 1.192 1.149 1.065 0.901 0.871 0.836 0.779
## Cumulative % of var. 85.064 86.213 87.278 88.179 89.049 89.885 90.664
## Dim.22 Dim.23 Dim.24 Dim.25 Dim.26 Dim.27 Dim.28
## Variance 10.976 9.943 9.717 9.265 8.420 7.804 7.735
## % of var. 0.721 0.653 0.638 0.608 0.553 0.513 0.508
## Cumulative % of var. 91.385 92.038 92.676 93.284 93.837 94.350 94.858
## Dim.29 Dim.30 Dim.31 Dim.32 Dim.33 Dim.34 Dim.35
## Variance 7.239 6.712 6.312 5.967 5.736 5.435 5.160
## % of var. 0.475 0.441 0.415 0.392 0.377 0.357 0.339
## Cumulative % of var. 95.333 95.774 96.189 96.580 96.957 97.314 97.653
## Dim.36 Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 4.607 4.424 4.334 4.064 3.639 3.362 3.133
## % of var. 0.303 0.291 0.285 0.267 0.239 0.221 0.206
## Cumulative % of var. 97.956 98.246 98.531 98.798 99.037 99.257 99.463
## Dim.43 Dim.44 Dim.45 Dim.46 Dim.47 Dim.48 Dim.49
## Variance 3.058 2.604 0.458 0.440 0.375 0.355 0.314
## % of var. 0.201 0.171 0.030 0.029 0.025 0.023 0.021
## Cumulative % of var. 99.664 99.835 99.865 99.894 99.918 99.942 99.962
## Dim.50 Dim.51
## Variance 0.293 0.279
## % of var. 0.019 0.018
## Cumulative % of var. 99.982 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 32.088 | -18.325 1.315 0.326 | -2.495 0.088 0.006
## 2 | 31.680 | -21.457 1.804 0.459 | -1.903 0.051 0.004
## 3 | 38.006 | -1.104 0.005 0.001 | 5.286 0.395 0.019
## 4 | 37.414 | -18.825 1.388 0.253 | -5.979 0.505 0.026
## 5 | 37.978 | -15.383 0.927 0.164 | -4.275 0.258 0.013
## 6 | 35.269 | -13.912 0.758 0.156 | -2.209 0.069 0.004
## 7 | 36.153 | -6.805 0.181 0.035 | 10.166 1.460 0.079
## 8 | 27.758 | -7.720 0.233 0.077 | -4.107 0.238 0.022
## 9 | 34.797 | -9.891 0.383 0.081 | 7.582 0.812 0.047
## 10 | 49.298 | 12.854 0.647 0.068 | 37.666 20.047 0.584
## Dim.3 ctr cos2
## 1 | -9.013 1.309 0.079 |
## 2 | -7.122 0.817 0.051 |
## 3 | -5.725 0.528 0.023 |
## 4 | -13.739 3.042 0.135 |
## 5 | -6.809 0.747 0.032 |
## 6 | -9.739 1.528 0.076 |
## 7 | -6.082 0.596 0.028 |
## 8 | 0.716 0.008 0.001 |
## 9 | -0.149 0.000 0.000 |
## 10 | 9.082 1.329 0.034 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 168.0777_0.606 | -0.020 0.000 0.000 | -0.847 0.527 0.306 | -0.231 0.045
## 154.0621_0.609 | -0.072 0.001 0.008 | -0.215 0.034 0.071 | -0.084 0.006
## 124.0072_0.616 | -0.091 0.002 0.006 | -0.645 0.305 0.292 | -0.169 0.024
## 193.0351_0.617 | 0.117 0.003 0.057 | 0.231 0.039 0.222 | 0.140 0.016
## 239.1144_0.622 | -0.083 0.001 0.005 | -0.464 0.158 0.151 | -0.119 0.012
## 195.051_0.627 | 0.006 0.000 0.000 | 0.143 0.015 0.137 | 0.026 0.001
## 215.0326_0.629 | -0.050 0.000 0.015 | 0.034 0.001 0.007 | 0.030 0.001
## 219.0457_0.629 | 0.045 0.000 0.003 | -0.136 0.014 0.028 | -0.145 0.018
## 217.0486_0.63 | 0.105 0.002 0.017 | -0.109 0.009 0.019 | -0.130 0.014
## 245.0433_0.63 | 0.062 0.001 0.010 | -0.093 0.006 0.021 | -0.076 0.005
## cos2
## 168.0777_0.606 0.023 |
## 154.0621_0.609 0.011 |
## 124.0072_0.616 0.020 |
## 193.0351_0.617 0.081 |
## 239.1144_0.622 0.010 |
## 195.051_0.627 0.004 |
## 215.0326_0.629 0.006 |
## 219.0457_0.629 0.032 |
## 217.0486_0.63 0.026 |
## 245.0433_0.63 0.014 |
##
## Supplementary categories (the 10 first)
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test
## 6101_U1_C18NEG_59 | 32.088 | -18.325 0.326 -0.827 | -2.495 0.006 -0.214
## 6101_U2_C18NEG_30 | 35.735 | -11.006 0.095 -0.497 | -11.322 0.100 -0.970
## 6101_U3_C18NEG_29 | 32.042 | -19.088 0.355 -0.861 | -4.063 0.016 -0.348
## 6101_U4_C18NEG_14 | 33.801 | -17.195 0.259 -0.776 | 0.568 0.000 0.049
## 6102_U1_C18NEG_26 | 31.680 | -21.457 0.459 -0.968 | -1.903 0.004 -0.163
## 6102_U2_C18NEG_16 | 31.723 | -10.452 0.109 -0.472 | -7.536 0.056 -0.646
## 6102_U3_C18NEG_48 | 36.424 | -11.925 0.107 -0.538 | 2.968 0.007 0.254
## 6102_U4_C18NEG_50 | 29.347 | -9.631 0.108 -0.435 | 6.099 0.043 0.523
## 6103_U1_C18NEG_21 | 38.006 | -1.104 0.001 -0.050 | 5.286 0.019 0.453
## 6103_U2_C18NEG_60 | 41.308 | -4.739 0.013 -0.214 | -6.264 0.023 -0.537
## Dim.3 cos2 v.test
## 6101_U1_C18NEG_59 | -9.013 0.079 -0.825 |
## 6101_U2_C18NEG_30 | 16.477 0.213 1.508 |
## 6101_U3_C18NEG_29 | -6.642 0.043 -0.608 |
## 6101_U4_C18NEG_14 | -9.807 0.084 -0.898 |
## 6102_U1_C18NEG_26 | -7.122 0.051 -0.652 |
## 6102_U2_C18NEG_16 | 18.240 0.331 1.670 |
## 6102_U3_C18NEG_48 | -3.587 0.010 -0.328 |
## 6102_U4_C18NEG_50 | -6.357 0.047 -0.582 |
## 6103_U1_C18NEG_21 | -5.725 0.023 -0.524 |
## 6103_U2_C18NEG_60 | 16.929 0.168 1.550 || Dist | Dim.1 | cos2 | v.test | Dim.2 | cos2 | v.test | Dim.3 | cos2 | v.test | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6101_U1_C18NEG_59 | | | 32.088 | | | -18.325 | 0.326 | -0.827 | | | -2.495 | 0.006 | -0.214 | | | -9.013 | 0.079 | -0.825 | | |
| 6101_U2_C18NEG_30 | | | 35.735 | | | -11.006 | 0.095 | -0.497 | | | -11.322 | 0.100 | -0.970 | | | 16.477 | 0.213 | 1.508 | | |
| 6101_U3_C18NEG_29 | | | 32.042 | | | -19.088 | 0.355 | -0.861 | | | -4.063 | 0.016 | -0.348 | | | -6.642 | 0.043 | -0.608 | | |
| 6101_U4_C18NEG_14 | | | 33.801 | | | -17.195 | 0.259 | -0.776 | | | 0.568 | 0.000 | 0.049 | | | -9.807 | 0.084 | -0.898 | | |
| 6102_U1_C18NEG_26 | | | 31.680 | | | -21.457 | 0.459 | -0.968 | | | -1.903 | 0.004 | -0.163 | | | -7.122 | 0.051 | -0.652 | | |
| 6102_U2_C18NEG_16 | | | 31.723 | | | -10.452 | 0.109 | -0.472 | | | -7.536 | 0.056 | -0.646 | | | 18.240 | 0.331 | 1.670 | | |
| 6102_U3_C18NEG_48 | | | 36.424 | | | -11.925 | 0.107 | -0.538 | | | 2.968 | 0.007 | 0.254 | | | -3.587 | 0.010 | -0.328 | | |
| 6102_U4_C18NEG_50 | | | 29.347 | | | -9.631 | 0.108 | -0.435 | | | 6.099 | 0.043 | 0.523 | | | -6.357 | 0.047 | -0.582 | | |
| 6103_U1_C18NEG_21 | | | 38.006 | | | -1.104 | 0.001 | -0.050 | | | 5.286 | 0.019 | 0.453 | | | -5.725 | 0.023 | -0.524 | | |
| 6103_U2_C18NEG_60 | | | 41.308 | | | -4.739 | 0.013 | -0.214 | | | -6.264 | 0.023 | -0.537 | | | 16.929 | 0.168 | 1.550 | | |
# pull PC coordinates into df
PC_no6106_QC_log2 <- as.data.frame(PCA.imp_no6106_log2$ind$coord)
# bind back metadata from cols 1-11
PC_no6106_QC_log2 <- bind_cols(imp_no6106_log2[,1:11], PC_no6106_QC_log2)
# grab some variance explained
importance_no6106_QC <- PCA.imp_no6106_log2$eig
# set variance explained with PC1, round to 2 digits
PC1_no6106_withQC <- round(importance_no6106_QC[1,2], 2)
# set variance explained with PC2, round to 2 digits
PC2_no6106_withQC <- round(importance_no6106_QC[2,2], 2)Plots
Using FactoExtra package
Manual scores plot
# manual scores plot
(PCA_no6106_withQCs <- PC_no6106_QC_log2 %>%
ggplot(aes(x = Dim.1, y = Dim.2,
fill = factor(Intervention, levels = c("Yellow", "Red", "QC")))) +
geom_point(shape = 21, alpha = 0.8) +
scale_fill_manual(values = c("gold", "tomato1", "light grey")) +
scale_color_manual(values = "black") +
theme_minimal() +
coord_fixed(PC2_no6106_withQC/PC1_no6106_withQC) +
labs(x = glue::glue("PC1: {PC1_no6106_withQC}%"),
y = glue::glue("PC2: {PC2_no6106_withQC}%"),
fill = "Group",
title = "Principal Components Analysis Scores Plot"))
Because of subject 6106, the pooled QC sample points are quite far from samples.
Without QCs
Wrangle
imp_no6106_log2_noQCs <- imp_metabind_clust_log2 %>%
filter(Intervention != "QC") %>%
filter(Subject != "6106")
PCA.imp_no6106_log2_noQCs <- PCA(imp_no6106_log2_noQCs, # wide data
quali.sup=1:11, # remove qualitative variables
graph=FALSE, # don't graph
scale.unit=FALSE) # don't scale, we already did this
# look at summary
kable(summary(PCA.imp_no6106_log2_noQCs))##
## Call:
## PCA(X = imp_no6106_log2_noQCs, scale.unit = FALSE, quali.sup = 1:11,
## graph = FALSE)
##
##
## Eigenvalues
## Dim.1 Dim.2 Dim.3 Dim.4 Dim.5 Dim.6 Dim.7
## Variance 187.189 144.909 119.427 100.210 83.708 72.848 55.450
## % of var. 14.777 11.440 9.428 7.911 6.608 5.751 4.377
## Cumulative % of var. 14.777 26.217 35.645 43.556 50.164 55.915 60.293
## Dim.8 Dim.9 Dim.10 Dim.11 Dim.12 Dim.13 Dim.14
## Variance 43.661 41.361 38.384 32.195 28.218 25.965 22.378
## % of var. 3.447 3.265 3.030 2.542 2.228 2.050 1.767
## Cumulative % of var. 63.739 67.005 70.035 72.577 74.804 76.854 78.621
## Dim.15 Dim.16 Dim.17 Dim.18 Dim.19 Dim.20 Dim.21
## Variance 21.162 20.528 17.264 15.700 15.575 14.082 12.966
## % of var. 1.671 1.621 1.363 1.239 1.230 1.112 1.024
## Cumulative % of var. 80.291 81.912 83.275 84.514 85.744 86.855 87.879
## Dim.22 Dim.23 Dim.24 Dim.25 Dim.26 Dim.27 Dim.28
## Variance 12.630 11.485 10.983 9.951 9.423 9.170 8.559
## % of var. 0.997 0.907 0.867 0.786 0.744 0.724 0.676
## Cumulative % of var. 88.876 89.783 90.650 91.435 92.179 92.903 93.579
## Dim.29 Dim.30 Dim.31 Dim.32 Dim.33 Dim.34 Dim.35
## Variance 7.928 7.614 7.072 6.774 6.443 6.108 5.462
## % of var. 0.626 0.601 0.558 0.535 0.509 0.482 0.431
## Cumulative % of var. 94.205 94.806 95.364 95.899 96.407 96.889 97.321
## Dim.36 Dim.37 Dim.38 Dim.39 Dim.40 Dim.41 Dim.42
## Variance 5.257 5.136 4.863 4.299 3.994 3.697 3.613
## % of var. 0.415 0.405 0.384 0.339 0.315 0.292 0.285
## Cumulative % of var. 97.736 98.141 98.525 98.864 99.180 99.472 99.757
## Dim.43
## Variance 3.080
## % of var. 0.243
## Cumulative % of var. 100.000
##
## Individuals (the 10 first)
## Dist Dim.1 ctr cos2 Dim.2 ctr cos2
## 1 | 28.465 | -11.080 1.491 0.152 | -9.026 1.278 0.101
## 2 | 26.955 | -12.031 1.757 0.199 | -8.245 1.066 0.094
## 3 | 38.522 | 7.798 0.738 0.041 | -5.090 0.406 0.017
## 4 | 34.338 | -15.275 2.833 0.198 | -12.098 2.295 0.124
## 5 | 35.646 | -10.572 1.357 0.088 | -6.256 0.614 0.031
## 6 | 32.947 | -7.048 0.603 0.046 | -8.413 1.110 0.065
## 7 | 35.348 | 7.424 0.669 0.044 | -8.796 1.213 0.062
## 8 | 26.862 | -3.745 0.170 0.019 | 2.158 0.073 0.006
## 9 | 33.313 | 3.704 0.167 0.012 | -3.854 0.233 0.013
## 10 | 51.476 | 43.217 22.676 0.705 | -3.689 0.213 0.005
## Dim.3 ctr cos2
## 1 | -0.351 0.002 0.000 |
## 2 | -6.000 0.685 0.050 |
## 3 | 24.920 11.817 0.418 |
## 4 | -1.030 0.020 0.001 |
## 5 | -7.127 0.967 0.040 |
## 6 | -4.461 0.379 0.018 |
## 7 | 14.663 4.091 0.172 |
## 8 | 3.087 0.181 0.013 |
## 9 | -7.605 1.101 0.052 |
## 10 | -13.745 3.595 0.071 |
##
## Variables (the 10 first)
## Dim.1 ctr cos2 Dim.2 ctr cos2 Dim.3 ctr
## 168.0777_0.606 | -0.816 0.356 0.242 | 0.146 0.015 0.008 | 0.572 0.274
## 154.0621_0.609 | -0.302 0.049 0.119 | -0.024 0.000 0.001 | 0.095 0.008
## 124.0072_0.616 | -0.754 0.304 0.341 | 0.058 0.002 0.002 | 0.108 0.010
## 193.0351_0.617 | 0.307 0.050 0.336 | 0.059 0.002 0.012 | 0.139 0.016
## 239.1144_0.622 | -0.463 0.114 0.128 | 0.082 0.005 0.004 | 0.458 0.176
## 195.051_0.627 | 0.129 0.009 0.094 | -0.039 0.001 0.009 | -0.048 0.002
## 215.0326_0.629 | 0.003 0.000 0.000 | 0.002 0.000 0.000 | -0.015 0.000
## 219.0457_0.629 | -0.128 0.009 0.022 | -0.071 0.003 0.007 | 0.061 0.003
## 217.0486_0.63 | -0.087 0.004 0.010 | -0.065 0.003 0.006 | 0.101 0.009
## 245.0433_0.63 | -0.071 0.003 0.011 | -0.025 0.000 0.001 | -0.077 0.005
## cos2
## 168.0777_0.606 0.119 |
## 154.0621_0.609 0.012 |
## 124.0072_0.616 0.007 |
## 193.0351_0.617 0.069 |
## 239.1144_0.622 0.125 |
## 195.051_0.627 0.013 |
## 215.0326_0.629 0.001 |
## 219.0457_0.629 0.005 |
## 217.0486_0.63 0.014 |
## 245.0433_0.63 0.013 |
##
## Supplementary categories (the 10 first)
## Dist Dim.1 cos2 v.test Dim.2 cos2 v.test
## 6101_U1_C18NEG_59 | 28.465 | -11.080 0.152 -0.810 | -9.026 0.101 -0.750
## 6101_U2_C18NEG_30 | 34.183 | -6.956 0.041 -0.508 | 19.045 0.310 1.582
## 6101_U3_C18NEG_29 | 28.133 | -11.824 0.177 -0.864 | -6.221 0.049 -0.517
## 6101_U4_C18NEG_14 | 30.605 | -8.045 0.069 -0.588 | -10.664 0.121 -0.886
## 6102_U1_C18NEG_26 | 26.955 | -12.031 0.199 -0.879 | -8.245 0.094 -0.685
## 6102_U2_C18NEG_16 | 29.988 | -3.370 0.013 -0.246 | 19.076 0.405 1.585
## 6102_U3_C18NEG_48 | 34.688 | -2.057 0.004 -0.150 | -5.481 0.025 -0.455
## 6102_U4_C18NEG_50 | 27.735 | 1.707 0.004 0.125 | -8.392 0.092 -0.697
## 6103_U1_C18NEG_21 | 38.522 | 7.798 0.041 0.570 | -5.090 0.017 -0.423
## 6103_U2_C18NEG_60 | 40.997 | 2.434 0.004 0.178 | 19.471 0.226 1.617
## Dim.3 cos2 v.test
## 6101_U1_C18NEG_59 | -0.351 0.000 -0.032 |
## 6101_U2_C18NEG_30 | 5.136 0.023 0.470 |
## 6101_U3_C18NEG_29 | -0.457 0.000 -0.042 |
## 6101_U4_C18NEG_14 | 1.819 0.004 0.166 |
## 6102_U1_C18NEG_26 | -6.000 0.050 -0.549 |
## 6102_U2_C18NEG_16 | 2.085 0.005 0.191 |
## 6102_U3_C18NEG_48 | -6.202 0.032 -0.568 |
## 6102_U4_C18NEG_50 | 5.317 0.037 0.487 |
## 6103_U1_C18NEG_21 | 24.920 0.418 2.280 |
## 6103_U2_C18NEG_60 | 24.867 0.368 2.275 || Dist | Dim.1 | cos2 | v.test | Dim.2 | cos2 | v.test | Dim.3 | cos2 | v.test | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6101_U1_C18NEG_59 | | | 28.465 | | | -11.080 | 0.152 | -0.810 | | | -9.026 | 0.101 | -0.750 | | | -0.351 | 0.000 | -0.032 | | |
| 6101_U2_C18NEG_30 | | | 34.183 | | | -6.956 | 0.041 | -0.508 | | | 19.045 | 0.310 | 1.582 | | | 5.136 | 0.023 | 0.470 | | |
| 6101_U3_C18NEG_29 | | | 28.133 | | | -11.824 | 0.177 | -0.864 | | | -6.221 | 0.049 | -0.517 | | | -0.457 | 0.000 | -0.042 | | |
| 6101_U4_C18NEG_14 | | | 30.605 | | | -8.045 | 0.069 | -0.588 | | | -10.664 | 0.121 | -0.886 | | | 1.819 | 0.004 | 0.166 | | |
| 6102_U1_C18NEG_26 | | | 26.955 | | | -12.031 | 0.199 | -0.879 | | | -8.245 | 0.094 | -0.685 | | | -6.000 | 0.050 | -0.549 | | |
| 6102_U2_C18NEG_16 | | | 29.988 | | | -3.370 | 0.013 | -0.246 | | | 19.076 | 0.405 | 1.585 | | | 2.085 | 0.005 | 0.191 | | |
| 6102_U3_C18NEG_48 | | | 34.688 | | | -2.057 | 0.004 | -0.150 | | | -5.481 | 0.025 | -0.455 | | | -6.202 | 0.032 | -0.568 | | |
| 6102_U4_C18NEG_50 | | | 27.735 | | | 1.707 | 0.004 | 0.125 | | | -8.392 | 0.092 | -0.697 | | | 5.317 | 0.037 | 0.487 | | |
| 6103_U1_C18NEG_21 | | | 38.522 | | | 7.798 | 0.041 | 0.570 | | | -5.090 | 0.017 | -0.423 | | | 24.920 | 0.418 | 2.280 | | |
| 6103_U2_C18NEG_60 | | | 40.997 | | | 2.434 | 0.004 | 0.178 | | | 19.471 | 0.226 | 1.617 | | | 24.867 | 0.368 | 2.275 | | |
# pull PC coordinates into df
PC_coord_no6106_noQC_log2 <- as.data.frame(PCA.imp_no6106_log2_noQCs$ind$coord)
# bind back metadata from cols 1-11
PC_coord_no6106_noQC_log2 <- bind_cols(imp_no6106_log2_noQCs[,1:11], PC_coord_no6106_noQC_log2)
# grab some variance explained
importance_no6106_noQC <- PCA.imp_no6106_log2_noQCs$eig
# set variance explained with PC1, round to 2 digits
PC1_no6106_noQC <- round(importance_no6106_noQC[1,2], 2)
# set variance explained with PC2, round to 2 digits
PC2_no6106_noQC <- round(importance_no6106_noQC[2,2], 2)Plots
Using FactoExtra
Manual scores plot
(PCA_no6106_withoutQCs <- PC_coord_no6106_noQC_log2 %>%
ggplot(aes(x = Dim.1,
y = Dim.2,
fill = Intervention)) +
geom_point(shape = 21, alpha = 0.8) +
geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
scale_fill_manual(values = c("tomato1", "gold")) +
scale_color_manual(values = "black") +
theme_minimal() +
coord_fixed(PC2_no6106_noQC/PC1_no6106_noQC) +
labs(x = glue::glue("PC1: {PC1_no6106_noQC}%"),
y = glue::glue("PC2: {PC2_no6106_noQC}%"),
fill = "Intervention",
title = "Principal Components Analysis Scores Plot"))
Now we’re seeing some separation along PC2! Let’s explore this more with a pub-friendly package for PCAs, the PCAtools package.
PCAtools
W/ Outlier
Data wrangling
# create rel abund df suitable for PCAtools package
imp_clust_omicsdata_outliers_forPCAtools <- as.data.frame(t(imp_clust)) # transpose df
names(imp_clust_omicsdata_outliers_forPCAtools) <- imp_clust_omicsdata_outliers_forPCAtools[1,] # make sample IDs column names
imp_clust_omicsdata_outliers_forPCAtools <- imp_clust_omicsdata_outliers_forPCAtools[-1,] # remove sample ID row
# create metadata df suitable for PCAtools pckg
metadata_outliers_forPCAtools <- metadata %>%
column_to_rownames(var = "sample_ID") # change sample ID column to rownames
# create a vector so that col names in abundance df matches metadata df
order_outliers_forPCAtools <- match(rownames(metadata_outliers_forPCAtools), colnames(imp_clust_omicsdata_outliers_forPCAtools))
# reorder col names in abundance df so that it matches metadata
abundata_outliers_reordered_forPCAtools <- imp_clust_omicsdata_outliers_forPCAtools[ ,order_outliers_forPCAtools]
# change abundance df to numeric
abundata_outliers_reordered_forPCAtools <- abundata_outliers_reordered_forPCAtools %>%
mutate_all(as.numeric)
# Log transform
log2_abundata_outliers_forPCAtools <- log2(abundata_outliers_reordered_forPCAtools)
# unite pre_post column with intervention column to create pre_intervention column
metadata_outliers_forPCAtools <- metadata_outliers_forPCAtools %>%
unite(col = "pre_post_intervention",
c("pre_post","Intervention"),
sep = "_",
remove = FALSE)PCA
# pca
p_outliers <- PCAtools::pca(log2_abundata_outliers_forPCAtools,
metadata = metadata_outliers_forPCAtools,
scale = FALSE # using scaled data already (log2 transformed)
)
(PCAtools_outliers <- biplot(p_outliers,
lab = paste0(metadata_outliers_forPCAtools$Subject),
colby = 'pre_post_intervention',
colkey = c("pre_Yellow" = "yellow",
"post_Yellow" = "yellow4",
"pre_Red" = "red",
"post_Red" = "red4"),
hline = 0, vline = 0,
legendPosition = 'right',
title = "PCA Scores Plot with Loadings",
subtitle = "Log2 transformed data, C18 (-), without QCs but with outliers",
ellipse = TRUE,
ellipseType = 't', # assumes multivariate
ellipseLevel = 0.95,
ellipseFill = TRUE,
ellipseAlpha = 0.2,
ellipseLineSize = 0,
showLoadings = TRUE))
We see here the top 5 metabolites driving separation along PC1, which is really separation between subject 6106 and the rest of the samples. These metabolites are worth me exploring a bit more as to why subject 6106 is an outlier. Let’s move on and explore the data without outliers more. Confidence ellipses were added to show that 6106 samples are statistical outliers.
No outliers
Data wrangling
# create rel abund df suitable for PCAtools package
imp_clust_omicsdata_forPCAtools <- as.data.frame(t(imp_clust)) # transpose df
names(imp_clust_omicsdata_forPCAtools) <- imp_clust_omicsdata_forPCAtools[1,] # make sample IDs column names
imp_clust_omicsdata_forPCAtools <- imp_clust_omicsdata_forPCAtools[-1,] # remove sample ID row
imp_clust_omicsdata_forPCAtools <- imp_clust_omicsdata_forPCAtools %>%
dplyr::select(!contains("QC")) # remove QC observations
# create metadata df suitable for PCAtools pckg
metadata_forPCAtools <- metadata %>%
column_to_rownames(var = "sample_ID") # change sample ID column to rownames
# create a vector so that col names in abundance df matches metadata df
order_forPCAtools <- match(rownames(metadata_forPCAtools), colnames(imp_clust_omicsdata_forPCAtools))
# reorder col names in abundance df so that it matches metadata
abundata_reordered_forPCAtools <- imp_clust_omicsdata_forPCAtools[ ,order_forPCAtools]
# change abundance df to numeric
abundata_reordered_forPCAtools <- abundata_reordered_forPCAtools %>%
mutate_all(as.numeric)
# Log transform
log2_abundata_forPCAtools <- log2(abundata_reordered_forPCAtools)
# remove 6106 subj from both df
log2_abundata_forPCAtools <- log2_abundata_forPCAtools %>%
dplyr::select(!contains("6106"))
metadata_forPCAtools <- metadata_forPCAtools %>%
filter(Subject != 6106)
# unite pre_post column with intervention column to create pre_intervention column
metadata_forPCAtools <- metadata_forPCAtools %>%
unite(col = "pre_post_intervention",
c("pre_post","Intervention"),
sep = "_",
remove = FALSE)Screeplot
Elbow method
# pca
p <- PCAtools::pca(log2_abundata_forPCAtools,
metadata = metadata_forPCAtools,
scale = FALSE # using scaled data already (log2 transformed)
)
elbow <- findElbowPoint(p$variance)
elbow## PC8
## 8 screeplot(p,
components = getComponents(p, 1:20),
vline = elbow) +
geom_label(aes(x = elbow + 1, y = 50,
label = 'Elbow method', vjust = -3, size = 8))
How many PCs do we need to capture at least 80% variance?
## PC15
## 15This shows we’d need quite a few PCs to capture most of the variance. Let’s move on with 2 PCs and explore more later.
PCA plots
w/ stats ellipses
PCAtools::biplot(p,
lab = paste0(metadata_forPCAtools$Subject),
colby = 'pre_post_intervention',
colkey = c("pre_Yellow" = "yellow",
"post_Yellow" = "yellow4",
"pre_Red" = "red",
"post_Red" = "red4"),
hline = 0, vline = 0,
# ellipse config
ellipse = TRUE,
ellipseType = 't', # assumes multivariate t-distribution
ellipseLevel = 0.95,
ellipseFill = TRUE,
ellipseAlpha = 0.2,
ellipseLineSize = 0,
xlim = c(-80,80), ylim = c(-40,40),
legendPosition = 'right',
title = "PCA Scores Plot",
subtitle = "Log2 transformed data, C18 (-), outliers removed, no QCs \n95% confidence level ellipses")
With confidence intervals (Hotelling’s t), most samples are within the intervals except for a few that are not far off. We will move forward.
w/ loadings
Let’s look at the metabolites driving
(PCA.colby.prevspost <- biplot(p,
lab = NULL,
# or lab = paste0(metadata_forPCAtools$Subject),
colby = 'pre_post_intervention',
colkey = c("pre_Yellow" = "yellow",
"post_Yellow" = "yellow4",
"pre_Red" = "red",
"post_Red" = "red4"),
hline = 0, vline = 0,
legendPosition = 'right',
title = "PCA Scores Plot",
subtitle = "Log2 transformed data, C18 (-), without QCs and outliers",
ylim = c(-45,45),
showLoadings = TRUE))
Feature 201.0228 at RT 4.555 drives the most separation along PC2 according to this PCA.
Pairs plots
Here, we will look at separations for several components at once using pairs plots.
post-intervention comparisons
(PCA_pairsplot.prevspost <-
pairsplot(p,
components = getComponents(p, c(1:10)),
triangle = TRUE, trianglelabSize = 12,
hline = 0, vline = 0,
pointSize = 0.4,
gridlines.major = FALSE, gridlines.minor = FALSE,
colby = 'pre_post_intervention',
colkey = c("pre_Yellow" = "yellow",
"post_Yellow" = "yellow4",
"pre_Red" = "pink",
"post_Red" = "red4"),
title = 'Pairs plot', plotaxes = FALSE,
margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm')))
sex?
Are there any obvious clusterings when colored by sex?
pairsplot(p,
components = getComponents(p, c(1:10)),
triangle = TRUE, trianglelabSize = 12,
hline = 0, vline = 0,
pointSize = 0.4,
gridlines.major = FALSE, gridlines.minor = FALSE,
colby = 'Sex',
colkey = c("M" = "red",
"F" = "purple"),
title = 'Pairs plot', plotaxes = FALSE,
margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))
sequence?
pairsplot(p,
components = getComponents(p, c(1:10)),
triangle = TRUE, trianglelabSize = 12,
hline = 0, vline = 0,
pointSize = 0.4,
gridlines.major = FALSE, gridlines.minor = FALSE,
colby = 'sequence',
title = 'Pairs plot', plotaxes = FALSE,
margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))
Eigen corplots
This is a cool way to explore the correlations between the metadata and the PCs! I want to look at how the metavariables correlate with PCs that account for 80% variation in the dataset.
Again: How many PCs do we need to capture at least 80% variance?
## PC15
## 15 eigencorplot(p,
components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]), # get components that account for 80% variance
metavars = colnames(metadata_forPCAtools),
col = c('darkblue', 'blue2', 'gray', 'red2', 'darkred'),
cexCorval = 0.7,
colCorval = 'white',
fontCorval = 2,
posLab = 'bottomleft',
rotLabX = 45,
posColKey = 'top',
cexLabColKey = 1.5,
scale = TRUE,
main = 'PC1-15 metadata correlations',
colFrame = 'white',
plotRsquared = FALSE)
eigencorplot(p,
components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]),
metavars = colnames(metadata_forPCAtools),
col = c('white', 'cornsilk1', 'gold', 'forestgreen', 'darkgreen'),
cexCorval = 1.2,
fontCorval = 2,
posLab = 'all',
rotLabX = 45,
scale = TRUE,
main = bquote(Principal ~ component ~ Pearson ~ r^2 ~ metadata ~ correlates),
plotRsquared = TRUE,
corFUN = 'pearson',
corUSE = 'pairwise.complete.obs',
corMultipleTestCorrection = 'BH',
signifSymbols = c('****', '***', '**', '*', ''),
signifCutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1))
I am most interested in PCs affected by pre_post_intervention, so I think it would be good to investigate the metabolites that contribute the most to these PCs.
Multilevel PCA
Because this is a crossover trial, measurements are taken from the same person repeatedly and thus introduces variance not dependent on the treatment but the subject. To account for interindividual variations in MVA, I try multilevel PCAs from the mixOmics package. Here, I do not remove subject 6106.
# make df suitable for mixOmics
Data_forMPCA <- imp_metabind_clust_log2_noQCs %>%
mutate_at("Subject", as.factor) # change subject id to factor
summary(as.factor(Data_forMPCA$Subject))## 6101 6102 6103 6104 6105 6106 6108 6109 6110 6111 6112 6113
## 4 4 4 4 4 4 4 4 4 4 4 4## [1] "sample_ID" "Subject" "Period"
## [4] "pre_post_intervention" "Intervention" "pre_post"
## [7] "sequence" "Intervention_week" "Sex"
## [10] "Age" "BMI"Regular scores plot
mixOmicsPCA.result <- mixOmics::pca(Data_forMPCA[,!names(Data_forMPCA) %in% metavar],
scale = FALSE,
center = FALSE)
plotIndiv(mixOmicsPCA.result,
ind.names = Data_forMPCA$Subject,
group = Data_forMPCA$pre_post_intervention,
legend = TRUE,
legend.title = "Treatment",
title = 'Regular PCA, C18 (-), Log2 transformed')
We see the same thing we’ve been seeing with 6106 overwhelming PC2. Let’s try the multilevel pca and see what happens!
Multilevel scores plot
multilevelPCA.result <- mixOmics::pca(Data_forMPCA[,-(c(1:11))],
multilevel = Data_forMPCA$Subject,
scale = FALSE,
center = FALSE)
plotIndiv(multilevelPCA.result,
ind.names = Data_forMPCA$Subject,
group = Data_forMPCA$pre_post_intervention,
legend = TRUE,
legend.title = "Treatment",
title = 'Multilevel PCA, C18 (-), Log2 transformed')
Now, even with 6106 still included, we see clear separation between post-Red and other timepoints! Let’s take a look at a loadings plot to see the top 40 features that contribute to PC1 separation.
Univariate analysis
Wrangle data
## # A tibble: 6 × 14
## sample_ID Subject Period pre_post_intervention Intervention pre_post sequence
## <chr> <chr> <chr> <chr> <chr> <chr> <chr>
## 1 6101_U1_C… 6101 U1 pre_Red Red pre R_Y
## 2 6101_U1_C… 6101 U1 pre_Red Red pre R_Y
## 3 6101_U1_C… 6101 U1 pre_Red Red pre R_Y
## 4 6101_U1_C… 6101 U1 pre_Red Red pre R_Y
## 5 6101_U1_C… 6101 U1 pre_Red Red pre R_Y
## 6 6101_U1_C… 6101 U1 pre_Red Red pre R_Y
## # ℹ 7 more variables: Intervention_week <chr>, Sex <chr>, Age <chr>, BMI <chr>,
## # mz_rt <chr>, rel_abund <dbl>, rel_abund_log2 <dbl># remove QCs
df_for_stats <- imp_metabind_clust_tidy_log2 %>%
filter(Intervention != "QC")
# check if QCs were removed
unique(df_for_stats$Intervention)## [1] "Red" "Yellow"# df without outlier 6106
df_for_stats_noOutlier <- df_for_stats %>%
filter(Subject != "6106")
# check if outlier was removed
unique(df_for_stats_noOutlier$Subject)## [1] "6101" "6102" "6103" "6104" "6105" "6108" "6109" "6110" "6111" "6112"
## [11] "6113"Parametric tests
ANOVA (repeated measures) across timepoints
anova_outpout_df <- df_for_stats_noOutlier %>%
dplyr::select(Subject, pre_post_intervention, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
anova_test(rel_abund_log2 ~ pre_post_intervention, wid = Subject,
detailed = TRUE) %>%
adjust_pvalue(method = "BH") %>%
as.data.frame()
anova_sig <- anova_outpout_df %>%
filter(p.adj <= .05)
# how many significant features?
nrow(anova_sig)## [1] 43Heatmap of features significant by ANOVA
# go back to wide for stats df
df_for_stats_wide_noOutlier <- df_for_stats_noOutlier %>%
dplyr::select(!rel_abund) %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2)
ANOVA_heatmap_data <- df_for_stats_wide_noOutlier %>%
unite("Subject_pre_post_intervention", Subject, pre_post_intervention, sep = "_", remove = FALSE) %>%
dplyr::select(Subject, Subject_pre_post_intervention, pre_post, all_of(anova_sig$mz_rt))
ANOVA_heatmap <-
pheatmap(ANOVA_heatmap_data[,-c(1:3)],
scale = "column",
cluster_rows = TRUE,
clustering_distance_rows = "euclidean",
clustering_distance_cols = "euclidean",
clustering_method = "ward.D2",
labels_row = ANOVA_heatmap_data$Subject_pre_post_intervention,
color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
main = "Heatmap of features significant across timepoints \nby repeated measures one-way ANOVA \nBenjamoni-Hochberg corrected p-values > 0.05 \nC18 (-)")
Paired t tests
Here, I am comparing pre- to post-intervention for both yellow and tomato soy (Red) juice interventions. I also compare post-yellow to post-red intervention. I am using the log transformed values of rel abundance since parametric tests assume normality.
Ctrl
# run paired t-tests for control intervention
ctrl_t.test_paired <- df_for_stats %>%
filter(Intervention == "Yellow") %>%
dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ pre_post,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
ctrl_t.test_paired_sig <- ctrl_t.test_paired %>%
filter(p <= 0.05)
kable(ctrl_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 131.0349_0.8 | rel_abund_log2 | post | pre | 12 | 12 | -2.461404 | 11 | 0.0316000 | * |
| 144.03_0.697 | rel_abund_log2 | post | pre | 12 | 12 | -2.225323 | 11 | 0.0479000 | * |
| 144.0665_0.781 | rel_abund_log2 | post | pre | 12 | 12 | -4.136737 | 11 | 0.0016500 | ** |
| 154.0621_0.609 | rel_abund_log2 | post | pre | 12 | 12 | -3.620947 | 11 | 0.0040200 | ** |
| 154.9984_0.703 | rel_abund_log2 | post | pre | 12 | 12 | -3.227300 | 11 | 0.0080500 | ** |
| 157.0506_2.959 | rel_abund_log2 | post | pre | 12 | 12 | -3.119834 | 11 | 0.0097500 | ** |
| 168.0777_0.606 | rel_abund_log2 | post | pre | 12 | 12 | -3.356714 | 11 | 0.0064000 | ** |
| 177.0225_0.776 | rel_abund_log2 | post | pre | 12 | 12 | -4.172454 | 11 | 0.0015600 | ** |
| 178.0524_2.721 | rel_abund_log2 | post | pre | 12 | 12 | 2.242762 | 11 | 0.0465000 | * |
| 185.0819_4.129 | rel_abund_log2 | post | pre | 12 | 12 | -2.975798 | 11 | 0.0126000 | * |
| 188.0021_0.665 | rel_abund_log2 | post | pre | 12 | 12 | -2.629418 | 11 | 0.0234000 | * |
| 191.0201_1.159 | rel_abund_log2 | post | pre | 12 | 12 | 2.457882 | 11 | 0.0318000 | * |
| 193.0353_5.465 | rel_abund_log2 | post | pre | 12 | 12 | -2.477664 | 11 | 0.0307000 | * |
| 197.0819_4.344 | rel_abund_log2 | post | pre | 12 | 12 | -2.405456 | 11 | 0.0349000 | * |
| 198.1135_5.021 | rel_abund_log2 | post | pre | 12 | 12 | -3.259088 | 11 | 0.0076100 | ** |
| 208.0646_0.766 | rel_abund_log2 | post | pre | 12 | 12 | -2.407311 | 11 | 0.0348000 | * |
| 212.0065_4.503 | rel_abund_log2 | post | pre | 12 | 12 | -5.338144 | 11 | 0.0002380 | *** |
| 215.0021_3.307 | rel_abund_log2 | post | pre | 12 | 12 | 4.806060 | 11 | 0.0005480 | *** |
| 217.0486_0.63 | rel_abund_log2 | post | pre | 12 | 12 | -4.509173 | 11 | 0.0008880 | *** |
| 217.1081_3.912 | rel_abund_log2 | post | pre | 12 | 12 | -2.700100 | 11 | 0.0207000 | * |
| 218.0492_0.684 | rel_abund_log2 | post | pre | 12 | 12 | -2.243466 | 11 | 0.0464000 | * |
| 219.0457_0.629 | rel_abund_log2 | post | pre | 12 | 12 | -2.750307 | 11 | 0.0189000 | * |
| 222.1134_5.811 | rel_abund_log2 | post | pre | 12 | 12 | -4.879452 | 11 | 0.0004870 | *** |
| 230.9963_3.838 | rel_abund_log2 | post | pre | 12 | 12 | 3.581533 | 11 | 0.0043100 | ** |
| 230.9968_3.178 | rel_abund_log2 | post | pre | 12 | 12 | 3.181807 | 11 | 0.0087300 | ** |
| 230.9969_3.457 | rel_abund_log2 | post | pre | 12 | 12 | 5.649514 | 11 | 0.0001490 | *** |
| 231.9926_0.79 | rel_abund_log2 | post | pre | 12 | 12 | -2.845334 | 11 | 0.0159000 | * |
| 235.057_0.641 | rel_abund_log2 | post | pre | 12 | 12 | -2.931184 | 11 | 0.0137000 | * |
| 239.1144_0.622 | rel_abund_log2 | post | pre | 12 | 12 | -2.576189 | 11 | 0.0258000 | * |
| 240.9816_3.419 | rel_abund_log2 | post | pre | 12 | 12 | 3.923128 | 11 | 0.0023800 | ** |
| 241.1189_3.536 | rel_abund_log2 | post | pre | 12 | 12 | -2.408506 | 11 | 0.0347000 | * |
| 241.1193_2.896 | rel_abund_log2 | post | pre | 12 | 12 | -2.671427 | 11 | 0.0217000 | * |
| 243.1344_3.787 | rel_abund_log2 | post | pre | 12 | 12 | -2.834514 | 11 | 0.0162000 | * |
| 243.1349_3.353 | rel_abund_log2 | post | pre | 12 | 12 | -3.345652 | 11 | 0.0065300 | ** |
| 246.0753_3.721 | rel_abund_log2 | post | pre | 12 | 12 | -2.692257 | 11 | 0.0209000 | * |
| 246.1531_5.399 | rel_abund_log2 | post | pre | 12 | 12 | -2.673427 | 11 | 0.0217000 | * |
| 253.9903_0.661 | rel_abund_log2 | post | pre | 12 | 12 | -3.090685 | 11 | 0.0103000 | * |
| 255.1349_3.614 | rel_abund_log2 | post | pre | 12 | 12 | -2.295478 | 11 | 0.0424000 | * |
| 255.1349_4.042 | rel_abund_log2 | post | pre | 12 | 12 | -2.306841 | 11 | 0.0415000 | * |
| 263.0272_2.81 | rel_abund_log2 | post | pre | 12 | 12 | 2.449753 | 11 | 0.0323000 | * |
| 264.9884_0.68 | rel_abund_log2 | post | pre | 12 | 12 | -2.661654 | 11 | 0.0221000 | * |
| 269.1505_4.575 | rel_abund_log2 | post | pre | 12 | 12 | -6.293448 | 11 | 0.0000589 | **** |
| 269.1506_4.275 | rel_abund_log2 | post | pre | 12 | 12 | -6.630859 | 11 | 0.0000371 | **** |
| 274.0396_0.676 | rel_abund_log2 | post | pre | 12 | 12 | -2.396063 | 11 | 0.0355000 | * |
| 274.039_0.769 | rel_abund_log2 | post | pre | 12 | 12 | -3.469477 | 11 | 0.0052400 | ** |
| 281.1142_4.039 | rel_abund_log2 | post | pre | 12 | 12 | -3.592610 | 11 | 0.0042200 | ** |
| 287.0526_0.66 | rel_abund_log2 | post | pre | 12 | 12 | -2.282729 | 11 | 0.0433000 | * |
| 299.1247_0.784 | rel_abund_log2 | post | pre | 12 | 12 | -5.775087 | 11 | 0.0001240 | *** |
| 300.039_0.661 | rel_abund_log2 | post | pre | 12 | 12 | -2.952846 | 11 | 0.0131000 | * |
| 302.1145_3.69 | rel_abund_log2 | post | pre | 12 | 12 | -2.989428 | 11 | 0.0123000 | * |
| 305.1604_5.512 | rel_abund_log2 | post | pre | 12 | 12 | -2.204836 | 11 | 0.0497000 | * |
| 310.9691_1.162 | rel_abund_log2 | post | pre | 12 | 12 | 2.470283 | 11 | 0.0311000 | * |
| 319.1032_0.789 | rel_abund_log2 | post | pre | 12 | 12 | -2.852395 | 11 | 0.0157000 | * |
| 319.1396_5.493 | rel_abund_log2 | post | pre | 12 | 12 | -2.373604 | 11 | 0.0369000 | * |
| 324.0723_0.776 | rel_abund_log2 | post | pre | 12 | 12 | -2.566481 | 11 | 0.0262000 | * |
| 336.072_3.762 | rel_abund_log2 | post | pre | 12 | 12 | -2.339190 | 11 | 0.0392000 | * |
| 336.1214_3.081 | rel_abund_log2 | post | pre | 12 | 12 | -3.104779 | 11 | 0.0100000 | ** |
| 337.0556_0.777 | rel_abund_log2 | post | pre | 12 | 12 | 3.076820 | 11 | 0.0105000 | * |
| 337.056_3.104 | rel_abund_log2 | post | pre | 12 | 12 | 3.143328 | 11 | 0.0093500 | ** |
| 345.1552_6.009 | rel_abund_log2 | post | pre | 12 | 12 | -3.309655 | 11 | 0.0069600 | ** |
| 345.1553_5.874 | rel_abund_log2 | post | pre | 12 | 12 | -3.653538 | 11 | 0.0038000 | ** |
| 350.088_3.366 | rel_abund_log2 | post | pre | 12 | 12 | -3.160283 | 11 | 0.0090700 | ** |
| 350.088_3.952 | rel_abund_log2 | post | pre | 12 | 12 | -3.124386 | 11 | 0.0096700 | ** |
| 354.1007_4.717 | rel_abund_log2 | post | pre | 12 | 12 | -3.341329 | 11 | 0.0065800 | ** |
| 354.1009_5.03 | rel_abund_log2 | post | pre | 12 | 12 | -2.980159 | 11 | 0.0125000 | * |
| 361.1501_4.225 | rel_abund_log2 | post | pre | 12 | 12 | -3.443159 | 11 | 0.0054900 | ** |
| 363.1657_4.345 | rel_abund_log2 | post | pre | 12 | 12 | -2.520770 | 11 | 0.0284000 | * |
| 366.1016_4.846 | rel_abund_log2 | post | pre | 12 | 12 | -2.373982 | 11 | 0.0369000 | * |
| 367.2236_4.071 | rel_abund_log2 | post | pre | 12 | 12 | -2.605583 | 11 | 0.0244000 | * |
| 369.1552_5.993 | rel_abund_log2 | post | pre | 12 | 12 | -3.862090 | 11 | 0.0026400 | ** |
| 369.1737_6.099 | rel_abund_log2 | post | pre | 12 | 12 | -2.239345 | 11 | 0.0468000 | * |
| 370.097_3.208 | rel_abund_log2 | post | pre | 12 | 12 | -2.414807 | 11 | 0.0343000 | * |
| 370.0981_3.747 | rel_abund_log2 | post | pre | 12 | 12 | -2.508531 | 11 | 0.0291000 | * |
| 372.1099_3.404 | rel_abund_log2 | post | pre | 12 | 12 | -2.869688 | 11 | 0.0153000 | * |
| 372.1116_3.914 | rel_abund_log2 | post | pre | 12 | 12 | -4.440271 | 11 | 0.0009950 | *** |
| 375.1294_4.074 | rel_abund_log2 | post | pre | 12 | 12 | -3.421851 | 11 | 0.0057000 | ** |
| 385.1498_5.181 | rel_abund_log2 | post | pre | 12 | 12 | -6.953732 | 11 | 0.0000241 | **** |
| 385.1685_4.355 | rel_abund_log2 | post | pre | 12 | 12 | -2.222944 | 11 | 0.0481000 | * |
| 387.1489_5.77 | rel_abund_log2 | post | pre | 12 | 12 | -2.981651 | 11 | 0.0125000 | * |
| 389.0981_4.564 | rel_abund_log2 | post | pre | 12 | 12 | -2.232397 | 11 | 0.0473000 | * |
| 393.2643_6.229 | rel_abund_log2 | post | pre | 12 | 12 | -2.492934 | 11 | 0.0299000 | * |
| 400.1429_4.669 | rel_abund_log2 | post | pre | 12 | 12 | -2.214383 | 11 | 0.0488000 | * |
| 403.1971_3.26 | rel_abund_log2 | post | pre | 12 | 12 | 2.535363 | 11 | 0.0277000 | * |
| 403.197_0.807 | rel_abund_log2 | post | pre | 12 | 12 | 3.049801 | 11 | 0.0111000 | * |
| 413.1436_3.2 | rel_abund_log2 | post | pre | 12 | 12 | 2.338890 | 11 | 0.0392000 | * |
| 418.1327_4.164 | rel_abund_log2 | post | pre | 12 | 12 | -2.471681 | 11 | 0.0310000 | * |
| 424.0493_0.721 | rel_abund_log2 | post | pre | 12 | 12 | -3.444872 | 11 | 0.0054800 | ** |
| 425.145_4.374 | rel_abund_log2 | post | pre | 12 | 12 | -2.505174 | 11 | 0.0292000 | * |
| 443.1557_5.98 | rel_abund_log2 | post | pre | 12 | 12 | -2.404597 | 11 | 0.0349000 | * |
| 446.2907_6.081 | rel_abund_log2 | post | pre | 12 | 12 | -2.202775 | 11 | 0.0498000 | * |
| 447.0919_4.504 | rel_abund_log2 | post | pre | 12 | 12 | 4.490515 | 11 | 0.0009150 | *** |
| 447.0921_4.067 | rel_abund_log2 | post | pre | 12 | 12 | 3.953049 | 11 | 0.0022600 | ** |
| 447.092_4.187 | rel_abund_log2 | post | pre | 12 | 12 | 2.793693 | 11 | 0.0175000 | * |
| 462.1766_4.998 | rel_abund_log2 | post | pre | 12 | 12 | -2.601817 | 11 | 0.0246000 | * |
| 464.0663_2.822 | rel_abund_log2 | post | pre | 12 | 12 | 3.187185 | 11 | 0.0086500 | ** |
| 465.2493_6.146 | rel_abund_log2 | post | pre | 12 | 12 | -2.876169 | 11 | 0.0151000 | * |
| 481.2439_5.242 | rel_abund_log2 | post | pre | 12 | 12 | -2.633719 | 11 | 0.0233000 | * |
| 496.198_6.048 | rel_abund_log2 | post | pre | 12 | 12 | -2.317192 | 11 | 0.0408000 | * |
| 499.0756_0.705 | rel_abund_log2 | post | pre | 12 | 12 | 3.107461 | 11 | 0.0099700 | ** |
| 509.2751_5.084 | rel_abund_log2 | post | pre | 12 | 12 | -3.310485 | 11 | 0.0069500 | ** |
| 509.2751_6.332 | rel_abund_log2 | post | pre | 12 | 12 | -2.868766 | 11 | 0.0153000 | * |
| 514.136_3.993 | rel_abund_log2 | post | pre | 12 | 12 | -2.752558 | 11 | 0.0188000 | * |
| 528.2633_5.962 | rel_abund_log2 | post | pre | 12 | 12 | -2.378530 | 11 | 0.0366000 | * |
| 541.2651_4.807 | rel_abund_log2 | post | pre | 12 | 12 | -2.543890 | 11 | 0.0273000 | * |
| 541.265_4.928 | rel_abund_log2 | post | pre | 12 | 12 | -2.271880 | 11 | 0.0442000 | * |
| 572.2528_4.339 | rel_abund_log2 | post | pre | 12 | 12 | -3.083599 | 11 | 0.0104000 | * |
| 600.2843_4.732 | rel_abund_log2 | post | pre | 12 | 12 | -3.348925 | 11 | 0.0064900 | ** |
| 613.3589_5.89 | rel_abund_log2 | post | pre | 12 | 12 | -2.670162 | 11 | 0.0218000 | * |
| 629.3537_5.37 | rel_abund_log2 | post | pre | 12 | 12 | -3.064920 | 11 | 0.0108000 | * |
| 636.1806_3.455 | rel_abund_log2 | post | pre | 12 | 12 | -2.560241 | 11 | 0.0265000 | * |
## [1] 110Ctrl - no outlier
# run paired t-tests for control intervention
ctrl_noOutlier_t.test_paired <- df_for_stats_noOutlier %>%
filter(Intervention == "Yellow") %>%
dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ pre_post,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
ctrl_noOutlier_t.test_paired_sig <- ctrl_noOutlier_t.test_paired %>%
filter(p <= 0.05)
kable(ctrl_noOutlier_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 144.03_0.697 | rel_abund_log2 | post | pre | 11 | 11 | -2.285890 | 10 | 0.0453000 | * |
| 144.0665_0.781 | rel_abund_log2 | post | pre | 11 | 11 | -4.220806 | 10 | 0.0017700 | ** |
| 154.0621_0.609 | rel_abund_log2 | post | pre | 11 | 11 | -3.543489 | 10 | 0.0053300 | ** |
| 154.9984_0.703 | rel_abund_log2 | post | pre | 11 | 11 | -2.805096 | 10 | 0.0186000 | * |
| 157.0506_2.959 | rel_abund_log2 | post | pre | 11 | 11 | -2.712416 | 10 | 0.0218000 | * |
| 168.0777_0.606 | rel_abund_log2 | post | pre | 11 | 11 | -2.925426 | 10 | 0.0152000 | * |
| 177.0225_0.776 | rel_abund_log2 | post | pre | 11 | 11 | -3.702294 | 10 | 0.0040900 | ** |
| 182.0282_0.77 | rel_abund_log2 | post | pre | 11 | 11 | 2.493735 | 10 | 0.0318000 | * |
| 185.0819_4.129 | rel_abund_log2 | post | pre | 11 | 11 | -2.576161 | 10 | 0.0276000 | * |
| 188.0021_0.665 | rel_abund_log2 | post | pre | 11 | 11 | -2.442009 | 10 | 0.0347000 | * |
| 191.0201_1.159 | rel_abund_log2 | post | pre | 11 | 11 | 2.454586 | 10 | 0.0340000 | * |
| 193.0353_5.465 | rel_abund_log2 | post | pre | 11 | 11 | -2.403408 | 10 | 0.0371000 | * |
| 198.1135_5.021 | rel_abund_log2 | post | pre | 11 | 11 | -2.876288 | 10 | 0.0165000 | * |
| 212.0065_4.503 | rel_abund_log2 | post | pre | 11 | 11 | -4.840716 | 10 | 0.0006810 | *** |
| 215.0021_3.307 | rel_abund_log2 | post | pre | 11 | 11 | 4.651521 | 10 | 0.0009060 | *** |
| 217.0486_0.63 | rel_abund_log2 | post | pre | 11 | 11 | -4.155849 | 10 | 0.0019600 | ** |
| 217.1081_3.912 | rel_abund_log2 | post | pre | 11 | 11 | -2.307828 | 10 | 0.0437000 | * |
| 218.0492_0.684 | rel_abund_log2 | post | pre | 11 | 11 | -2.678661 | 10 | 0.0231000 | * |
| 219.0457_0.629 | rel_abund_log2 | post | pre | 11 | 11 | -2.443180 | 10 | 0.0347000 | * |
| 222.1134_5.811 | rel_abund_log2 | post | pre | 11 | 11 | -4.446102 | 10 | 0.0012400 | ** |
| 230.9963_3.838 | rel_abund_log2 | post | pre | 11 | 11 | 3.246327 | 10 | 0.0087700 | ** |
| 230.9968_3.178 | rel_abund_log2 | post | pre | 11 | 11 | 2.757680 | 10 | 0.0202000 | * |
| 230.9969_3.457 | rel_abund_log2 | post | pre | 11 | 11 | 5.185649 | 10 | 0.0004100 | *** |
| 231.9926_0.79 | rel_abund_log2 | post | pre | 11 | 11 | -2.441984 | 10 | 0.0347000 | * |
| 235.057_0.641 | rel_abund_log2 | post | pre | 11 | 11 | -2.517797 | 10 | 0.0305000 | * |
| 240.9816_3.419 | rel_abund_log2 | post | pre | 11 | 11 | 3.896096 | 10 | 0.0029800 | ** |
| 241.1193_2.896 | rel_abund_log2 | post | pre | 11 | 11 | -2.268062 | 10 | 0.0467000 | * |
| 243.1344_3.787 | rel_abund_log2 | post | pre | 11 | 11 | -2.433479 | 10 | 0.0352000 | * |
| 243.1349_3.353 | rel_abund_log2 | post | pre | 11 | 11 | -2.933145 | 10 | 0.0150000 | * |
| 246.0753_3.721 | rel_abund_log2 | post | pre | 11 | 11 | -2.721525 | 10 | 0.0215000 | * |
| 246.1531_5.399 | rel_abund_log2 | post | pre | 11 | 11 | -2.478294 | 10 | 0.0326000 | * |
| 253.9903_0.661 | rel_abund_log2 | post | pre | 11 | 11 | -3.283821 | 10 | 0.0082400 | ** |
| 264.9884_0.68 | rel_abund_log2 | post | pre | 11 | 11 | -2.561411 | 10 | 0.0283000 | * |
| 267.1235_7.063 | rel_abund_log2 | post | pre | 11 | 11 | 2.609438 | 10 | 0.0261000 | * |
| 269.1505_4.575 | rel_abund_log2 | post | pre | 11 | 11 | -5.779048 | 10 | 0.0001780 | *** |
| 269.1506_4.275 | rel_abund_log2 | post | pre | 11 | 11 | -5.999654 | 10 | 0.0001320 | *** |
| 274.0396_0.676 | rel_abund_log2 | post | pre | 11 | 11 | -2.253681 | 10 | 0.0479000 | * |
| 274.039_0.769 | rel_abund_log2 | post | pre | 11 | 11 | -3.305777 | 10 | 0.0079400 | ** |
| 281.1142_4.039 | rel_abund_log2 | post | pre | 11 | 11 | -3.150143 | 10 | 0.0103000 | * |
| 287.0227_3.548 | rel_abund_log2 | post | pre | 11 | 11 | -2.635784 | 10 | 0.0249000 | * |
| 297.0975_4.498 | rel_abund_log2 | post | pre | 11 | 11 | -2.506894 | 10 | 0.0311000 | * |
| 299.1247_0.784 | rel_abund_log2 | post | pre | 11 | 11 | -6.018921 | 10 | 0.0001290 | *** |
| 300.039_0.661 | rel_abund_log2 | post | pre | 11 | 11 | -2.539255 | 10 | 0.0294000 | * |
| 302.1145_3.69 | rel_abund_log2 | post | pre | 11 | 11 | -2.715408 | 10 | 0.0217000 | * |
| 310.9691_1.162 | rel_abund_log2 | post | pre | 11 | 11 | 2.935762 | 10 | 0.0149000 | * |
| 319.1032_0.789 | rel_abund_log2 | post | pre | 11 | 11 | -2.912620 | 10 | 0.0155000 | * |
| 324.0723_0.776 | rel_abund_log2 | post | pre | 11 | 11 | -2.234646 | 10 | 0.0495000 | * |
| 336.1214_3.081 | rel_abund_log2 | post | pre | 11 | 11 | -2.831634 | 10 | 0.0178000 | * |
| 337.0556_0.777 | rel_abund_log2 | post | pre | 11 | 11 | 2.859345 | 10 | 0.0170000 | * |
| 337.056_3.104 | rel_abund_log2 | post | pre | 11 | 11 | 3.172896 | 10 | 0.0099400 | ** |
| 345.1552_6.009 | rel_abund_log2 | post | pre | 11 | 11 | -3.023647 | 10 | 0.0128000 | * |
| 345.1553_5.874 | rel_abund_log2 | post | pre | 11 | 11 | -3.347115 | 10 | 0.0074000 | ** |
| 350.088_3.366 | rel_abund_log2 | post | pre | 11 | 11 | -2.922181 | 10 | 0.0152000 | * |
| 350.088_3.952 | rel_abund_log2 | post | pre | 11 | 11 | -2.915616 | 10 | 0.0154000 | * |
| 354.1007_4.717 | rel_abund_log2 | post | pre | 11 | 11 | -2.911973 | 10 | 0.0155000 | * |
| 354.1009_5.03 | rel_abund_log2 | post | pre | 11 | 11 | -2.607041 | 10 | 0.0262000 | * |
| 361.1501_4.225 | rel_abund_log2 | post | pre | 11 | 11 | -3.399428 | 10 | 0.0067800 | ** |
| 363.1657_4.345 | rel_abund_log2 | post | pre | 11 | 11 | -2.477080 | 10 | 0.0327000 | * |
| 367.2236_4.071 | rel_abund_log2 | post | pre | 11 | 11 | -2.368831 | 10 | 0.0394000 | * |
| 369.1552_5.993 | rel_abund_log2 | post | pre | 11 | 11 | -3.741100 | 10 | 0.0038400 | ** |
| 372.1099_3.404 | rel_abund_log2 | post | pre | 11 | 11 | -2.587993 | 10 | 0.0270000 | * |
| 372.1116_3.914 | rel_abund_log2 | post | pre | 11 | 11 | -3.964124 | 10 | 0.0026700 | ** |
| 375.1294_4.074 | rel_abund_log2 | post | pre | 11 | 11 | -2.987505 | 10 | 0.0136000 | * |
| 385.1498_5.181 | rel_abund_log2 | post | pre | 11 | 11 | -6.316287 | 10 | 0.0000872 | **** |
| 385.1685_4.355 | rel_abund_log2 | post | pre | 11 | 11 | -2.247770 | 10 | 0.0484000 | * |
| 387.1489_5.77 | rel_abund_log2 | post | pre | 11 | 11 | -2.774909 | 10 | 0.0196000 | * |
| 389.0981_4.564 | rel_abund_log2 | post | pre | 11 | 11 | -2.501829 | 10 | 0.0313000 | * |
| 393.2643_6.229 | rel_abund_log2 | post | pre | 11 | 11 | -2.248175 | 10 | 0.0483000 | * |
| 403.1971_3.26 | rel_abund_log2 | post | pre | 11 | 11 | 2.296263 | 10 | 0.0445000 | * |
| 403.197_0.807 | rel_abund_log2 | post | pre | 11 | 11 | 3.529688 | 10 | 0.0054500 | ** |
| 413.1436_3.2 | rel_abund_log2 | post | pre | 11 | 11 | 2.380349 | 10 | 0.0386000 | * |
| 424.0493_0.721 | rel_abund_log2 | post | pre | 11 | 11 | -3.169279 | 10 | 0.0100000 | ** |
| 425.145_4.374 | rel_abund_log2 | post | pre | 11 | 11 | -2.852678 | 10 | 0.0172000 | * |
| 447.0919_4.504 | rel_abund_log2 | post | pre | 11 | 11 | 4.046660 | 10 | 0.0023400 | ** |
| 447.0921_4.067 | rel_abund_log2 | post | pre | 11 | 11 | 4.041824 | 10 | 0.0023500 | ** |
| 447.092_4.187 | rel_abund_log2 | post | pre | 11 | 11 | 2.827069 | 10 | 0.0179000 | * |
| 464.0663_2.822 | rel_abund_log2 | post | pre | 11 | 11 | 2.983888 | 10 | 0.0137000 | * |
| 465.2493_6.146 | rel_abund_log2 | post | pre | 11 | 11 | -2.463089 | 10 | 0.0335000 | * |
| 481.2439_5.242 | rel_abund_log2 | post | pre | 11 | 11 | -2.520135 | 10 | 0.0304000 | * |
| 496.198_6.048 | rel_abund_log2 | post | pre | 11 | 11 | -2.370839 | 10 | 0.0392000 | * |
| 499.0756_0.705 | rel_abund_log2 | post | pre | 11 | 11 | 2.710151 | 10 | 0.0219000 | * |
| 509.2751_5.084 | rel_abund_log2 | post | pre | 11 | 11 | -2.989133 | 10 | 0.0136000 | * |
| 509.2751_6.332 | rel_abund_log2 | post | pre | 11 | 11 | -2.464232 | 10 | 0.0334000 | * |
| 514.136_3.993 | rel_abund_log2 | post | pre | 11 | 11 | -2.391987 | 10 | 0.0378000 | * |
| 528.2633_5.962 | rel_abund_log2 | post | pre | 11 | 11 | -2.784253 | 10 | 0.0193000 | * |
| 541.2651_4.807 | rel_abund_log2 | post | pre | 11 | 11 | -2.830511 | 10 | 0.0178000 | * |
| 541.265_4.928 | rel_abund_log2 | post | pre | 11 | 11 | -2.864772 | 10 | 0.0168000 | * |
| 572.2528_4.339 | rel_abund_log2 | post | pre | 11 | 11 | -3.222185 | 10 | 0.0091400 | ** |
| 576.1384_0.705 | rel_abund_log2 | post | pre | 11 | 11 | 2.735908 | 10 | 0.0210000 | * |
| 600.2843_4.732 | rel_abund_log2 | post | pre | 11 | 11 | -3.527558 | 10 | 0.0054700 | ** |
| 613.3589_5.89 | rel_abund_log2 | post | pre | 11 | 11 | -2.308321 | 10 | 0.0436000 | * |
| 629.3537_5.37 | rel_abund_log2 | post | pre | 11 | 11 | -2.773299 | 10 | 0.0197000 | * |
| 636.1806_3.455 | rel_abund_log2 | post | pre | 11 | 11 | -2.448178 | 10 | 0.0344000 | * |
## [1] 93Red
# run paired t-tests for control intervention
red_t.test_paired <- df_for_stats %>%
filter(Intervention == "Red") %>%
dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ pre_post,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
red_t.test_paired_sig <- red_t.test_paired %>%
filter(p <= 0.05)
kable(red_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1122.0248_4.125 | rel_abund_log2 | post | pre | 12 | 12 | 17.060205 | 11 | 0.0000000 | **** |
| 1122.0249_4.032 | rel_abund_log2 | post | pre | 12 | 12 | 13.546362 | 11 | 0.0000000 | **** |
| 168.0302_2.597 | rel_abund_log2 | post | pre | 12 | 12 | 3.008485 | 11 | 0.0119000 | * |
| 174.0405_0.655 | rel_abund_log2 | post | pre | 12 | 12 | -2.342576 | 11 | 0.0390000 | * |
| 187.0383_0.634 | rel_abund_log2 | post | pre | 12 | 12 | -2.870653 | 11 | 0.0152000 | * |
| 195.051_0.627 | rel_abund_log2 | post | pre | 12 | 12 | -3.036992 | 11 | 0.0113000 | * |
| 199.0068_4.328 | rel_abund_log2 | post | pre | 12 | 12 | 3.072751 | 11 | 0.0106000 | * |
| 201.0227_4.086 | rel_abund_log2 | post | pre | 12 | 12 | 15.310096 | 11 | 0.0000000 | **** |
| 201.0228_4.555 | rel_abund_log2 | post | pre | 12 | 12 | 16.566658 | 11 | 0.0000000 | **** |
| 203.0021_0.801 | rel_abund_log2 | post | pre | 12 | 12 | 2.248025 | 11 | 0.0460000 | * |
| 208.0615_3.836 | rel_abund_log2 | post | pre | 12 | 12 | -3.159004 | 11 | 0.0091000 | ** |
| 209.0532_3.773 | rel_abund_log2 | post | pre | 12 | 12 | -2.217854 | 11 | 0.0485000 | * |
| 211.0611_3.158 | rel_abund_log2 | post | pre | 12 | 12 | -2.937554 | 11 | 0.0135000 | * |
| 215.0021_3.307 | rel_abund_log2 | post | pre | 12 | 12 | 7.649012 | 11 | 0.0000100 | **** |
| 217.0172_4.4 | rel_abund_log2 | post | pre | 12 | 12 | 5.889523 | 11 | 0.0001050 | *** |
| 217.0173_3.88 | rel_abund_log2 | post | pre | 12 | 12 | 3.774771 | 11 | 0.0030700 | ** |
| 218.0821_5.22 | rel_abund_log2 | post | pre | 12 | 12 | -2.998352 | 11 | 0.0121000 | * |
| 223.0623_0.802 | rel_abund_log2 | post | pre | 12 | 12 | -2.597176 | 11 | 0.0248000 | * |
| 230.9967_0.757 | rel_abund_log2 | post | pre | 12 | 12 | 2.995785 | 11 | 0.0122000 | * |
| 230.9968_3.178 | rel_abund_log2 | post | pre | 12 | 12 | 2.284174 | 11 | 0.0432000 | * |
| 232.9759_0.708 | rel_abund_log2 | post | pre | 12 | 12 | 3.748385 | 11 | 0.0032200 | ** |
| 235.057_0.641 | rel_abund_log2 | post | pre | 12 | 12 | -2.738905 | 11 | 0.0193000 | * |
| 238.0776_4.607 | rel_abund_log2 | post | pre | 12 | 12 | -2.413731 | 11 | 0.0344000 | * |
| 246.0753_3.721 | rel_abund_log2 | post | pre | 12 | 12 | -2.743353 | 11 | 0.0191000 | * |
| 247.0798_4.12 | rel_abund_log2 | post | pre | 12 | 12 | -2.630902 | 11 | 0.0234000 | * |
| 253.0503_5.048 | rel_abund_log2 | post | pre | 12 | 12 | 7.606059 | 11 | 0.0000105 | **** |
| 254.9816_0.664 | rel_abund_log2 | post | pre | 12 | 12 | -2.302326 | 11 | 0.0419000 | * |
| 255.0674_5.078 | rel_abund_log2 | post | pre | 12 | 12 | 3.987767 | 11 | 0.0021300 | ** |
| 257.0816_6.056 | rel_abund_log2 | post | pre | 12 | 12 | 7.184325 | 11 | 0.0000179 | **** |
| 263.1041_2.796 | rel_abund_log2 | post | pre | 12 | 12 | 2.252658 | 11 | 0.0457000 | * |
| 275.0224_3.427 | rel_abund_log2 | post | pre | 12 | 12 | -2.326554 | 11 | 0.0401000 | * |
| 278.0698_0.683 | rel_abund_log2 | post | pre | 12 | 12 | -3.576924 | 11 | 0.0043400 | ** |
| 287.0526_0.66 | rel_abund_log2 | post | pre | 12 | 12 | -2.666736 | 11 | 0.0219000 | * |
| 295.0806_3.721 | rel_abund_log2 | post | pre | 12 | 12 | 3.044795 | 11 | 0.0112000 | * |
| 295.0815_4.242 | rel_abund_log2 | post | pre | 12 | 12 | 4.053748 | 11 | 0.0019000 | ** |
| 297.0975_4.072 | rel_abund_log2 | post | pre | 12 | 12 | 13.018491 | 11 | 0.0000001 | **** |
| 297.0975_4.498 | rel_abund_log2 | post | pre | 12 | 12 | 16.296093 | 11 | 0.0000000 | **** |
| 303.0177_0.795 | rel_abund_log2 | post | pre | 12 | 12 | -2.483713 | 11 | 0.0304000 | * |
| 319.0474_0.696 | rel_abund_log2 | post | pre | 12 | 12 | -2.628246 | 11 | 0.0235000 | * |
| 319.1032_0.789 | rel_abund_log2 | post | pre | 12 | 12 | -2.927694 | 11 | 0.0137000 | * |
| 331.1218_3.932 | rel_abund_log2 | post | pre | 12 | 12 | -3.072720 | 11 | 0.0106000 | * |
| 333.0047_4.33 | rel_abund_log2 | post | pre | 12 | 12 | 8.334242 | 11 | 0.0000044 | **** |
| 333.0066_3.893 | rel_abund_log2 | post | pre | 12 | 12 | 6.532428 | 11 | 0.0000424 | **** |
| 335.0244_4.36 | rel_abund_log2 | post | pre | 12 | 12 | 10.246418 | 11 | 0.0000006 | **** |
| 336.0718_3.292 | rel_abund_log2 | post | pre | 12 | 12 | -2.299021 | 11 | 0.0421000 | * |
| 343.1396_5.368 | rel_abund_log2 | post | pre | 12 | 12 | -3.516853 | 11 | 0.0048300 | ** |
| 345.1554_3.382 | rel_abund_log2 | post | pre | 12 | 12 | -2.853829 | 11 | 0.0157000 | * |
| 345.1554_4.506 | rel_abund_log2 | post | pre | 12 | 12 | -3.712369 | 11 | 0.0034300 | ** |
| 345.1933_4.201 | rel_abund_log2 | post | pre | 12 | 12 | -2.376027 | 11 | 0.0368000 | * |
| 347.0774_4.782 | rel_abund_log2 | post | pre | 12 | 12 | 2.308452 | 11 | 0.0414000 | * |
| 351.0199_0.667 | rel_abund_log2 | post | pre | 12 | 12 | -2.511641 | 11 | 0.0289000 | * |
| 355.1037_4.621 | rel_abund_log2 | post | pre | 12 | 12 | -3.629476 | 11 | 0.0039600 | ** |
| 359.1344_4.351 | rel_abund_log2 | post | pre | 12 | 12 | -3.000783 | 11 | 0.0121000 | * |
| 359.1353_0.806 | rel_abund_log2 | post | pre | 12 | 12 | -2.918482 | 11 | 0.0140000 | * |
| 359.1353_3.002 | rel_abund_log2 | post | pre | 12 | 12 | -2.904307 | 11 | 0.0143000 | * |
| 361.1499_3.066 | rel_abund_log2 | post | pre | 12 | 12 | -2.908186 | 11 | 0.0142000 | * |
| 361.1502_3.49 | rel_abund_log2 | post | pre | 12 | 12 | -2.779998 | 11 | 0.0179000 | * |
| 363.021_3.933 | rel_abund_log2 | post | pre | 12 | 12 | 4.596203 | 11 | 0.0007700 | *** |
| 363.0234_4.453 | rel_abund_log2 | post | pre | 12 | 12 | 3.694581 | 11 | 0.0035300 | ** |
| 365.0863_4.095 | rel_abund_log2 | post | pre | 12 | 12 | 8.786162 | 11 | 0.0000026 | **** |
| 366.0851_4.55 | rel_abund_log2 | post | pre | 12 | 12 | 5.065946 | 11 | 0.0003630 | *** |
| 369.0829_3.476 | rel_abund_log2 | post | pre | 12 | 12 | -2.506415 | 11 | 0.0292000 | * |
| 370.0972_3.765 | rel_abund_log2 | post | pre | 12 | 12 | -3.608558 | 11 | 0.0041100 | ** |
| 370.097_3.208 | rel_abund_log2 | post | pre | 12 | 12 | -2.962376 | 11 | 0.0129000 | * |
| 371.0976_0.801 | rel_abund_log2 | post | pre | 12 | 12 | -2.356344 | 11 | 0.0381000 | * |
| 371.0978_3.481 | rel_abund_log2 | post | pre | 12 | 12 | -2.817780 | 11 | 0.0167000 | * |
| 372.0951_0.795 | rel_abund_log2 | post | pre | 12 | 12 | -3.171804 | 11 | 0.0088900 | ** |
| 373.1141_3.685 | rel_abund_log2 | post | pre | 12 | 12 | -3.196332 | 11 | 0.0085100 | ** |
| 375.1278_3.109 | rel_abund_log2 | post | pre | 12 | 12 | -2.862459 | 11 | 0.0154000 | * |
| 375.1294_0.798 | rel_abund_log2 | post | pre | 12 | 12 | -2.752042 | 11 | 0.0188000 | * |
| 385.1685_4.355 | rel_abund_log2 | post | pre | 12 | 12 | -2.324504 | 11 | 0.0403000 | * |
| 393.0495_3.605 | rel_abund_log2 | post | pre | 12 | 12 | 7.082995 | 11 | 0.0000204 | **** |
| 393.0776_3.464 | rel_abund_log2 | post | pre | 12 | 12 | -2.746802 | 11 | 0.0190000 | * |
| 393.2643_6.229 | rel_abund_log2 | post | pre | 12 | 12 | -3.544331 | 11 | 0.0046000 | ** |
| 395.0628_0.675 | rel_abund_log2 | post | pre | 12 | 12 | -2.208983 | 11 | 0.0493000 | * |
| 396.1124_4.459 | rel_abund_log2 | post | pre | 12 | 12 | -2.268141 | 11 | 0.0445000 | * |
| 397.0582_0.701 | rel_abund_log2 | post | pre | 12 | 12 | -3.189085 | 11 | 0.0086200 | ** |
| 403.1133_4.514 | rel_abund_log2 | post | pre | 12 | 12 | -3.173482 | 11 | 0.0088600 | ** |
| 411.2021_4.884 | rel_abund_log2 | post | pre | 12 | 12 | -2.709305 | 11 | 0.0203000 | * |
| 413.1426_4.219 | rel_abund_log2 | post | pre | 12 | 12 | -3.588145 | 11 | 0.0042600 | ** |
| 413.1426_4.51 | rel_abund_log2 | post | pre | 12 | 12 | -4.228006 | 11 | 0.0014200 | ** |
| 413.1436_3.2 | rel_abund_log2 | post | pre | 12 | 12 | -2.225186 | 11 | 0.0479000 | * |
| 415.1607_0.797 | rel_abund_log2 | post | pre | 12 | 12 | -2.211185 | 11 | 0.0491000 | * |
| 418.1149_0.722 | rel_abund_log2 | post | pre | 12 | 12 | -3.062973 | 11 | 0.0108000 | * |
| 424.0493_0.721 | rel_abund_log2 | post | pre | 12 | 12 | 2.416979 | 11 | 0.0342000 | * |
| 427.197_3.64 | rel_abund_log2 | post | pre | 12 | 12 | -3.013185 | 11 | 0.0118000 | * |
| 427.197_4.049 | rel_abund_log2 | post | pre | 12 | 12 | -3.246928 | 11 | 0.0077800 | ** |
| 429.0815_4.032 | rel_abund_log2 | post | pre | 12 | 12 | 11.599019 | 11 | 0.0000002 | **** |
| 429.082_3.756 | rel_abund_log2 | post | pre | 12 | 12 | 10.983918 | 11 | 0.0000003 | **** |
| 429.083_3.201 | rel_abund_log2 | post | pre | 12 | 12 | 10.488952 | 11 | 0.0000005 | **** |
| 429.1762_5.798 | rel_abund_log2 | post | pre | 12 | 12 | 2.269644 | 11 | 0.0443000 | * |
| 431.0534_0.666 | rel_abund_log2 | post | pre | 12 | 12 | -3.193776 | 11 | 0.0085500 | ** |
| 431.0979_3.907 | rel_abund_log2 | post | pre | 12 | 12 | 11.128933 | 11 | 0.0000003 | **** |
| 433.0646_4.045 | rel_abund_log2 | post | pre | 12 | 12 | 4.163031 | 11 | 0.0015800 | ** |
| 433.1136_4.519 | rel_abund_log2 | post | pre | 12 | 12 | 5.189178 | 11 | 0.0002990 | *** |
| 433.113_4.886 | rel_abund_log2 | post | pre | 12 | 12 | 11.902482 | 11 | 0.0000001 | **** |
| 434.1396_3.922 | rel_abund_log2 | post | pre | 12 | 12 | -2.518498 | 11 | 0.0286000 | * |
| 445.0782_4.484 | rel_abund_log2 | post | pre | 12 | 12 | 7.998427 | 11 | 0.0000065 | **** |
| 447.0919_4.504 | rel_abund_log2 | post | pre | 12 | 12 | 3.234507 | 11 | 0.0079500 | ** |
| 447.0921_4.067 | rel_abund_log2 | post | pre | 12 | 12 | 2.881073 | 11 | 0.0149000 | * |
| 447.0928_3.777 | rel_abund_log2 | post | pre | 12 | 12 | 5.356682 | 11 | 0.0002310 | *** |
| 447.092_4.187 | rel_abund_log2 | post | pre | 12 | 12 | 3.199700 | 11 | 0.0084600 | ** |
| 449.0698_2.589 | rel_abund_log2 | post | pre | 12 | 12 | 2.237888 | 11 | 0.0469000 | * |
| 449.1074_4.301 | rel_abund_log2 | post | pre | 12 | 12 | 6.255445 | 11 | 0.0000621 | **** |
| 449.254_8.044 | rel_abund_log2 | post | pre | 12 | 12 | -2.928850 | 11 | 0.0137000 | * |
| 459.0843_2.964 | rel_abund_log2 | post | pre | 12 | 12 | 2.842769 | 11 | 0.0160000 | * |
| 459.093_3.801 | rel_abund_log2 | post | pre | 12 | 12 | 17.065127 | 11 | 0.0000000 | **** |
| 463.2334_5.897 | rel_abund_log2 | post | pre | 12 | 12 | -2.912403 | 11 | 0.0141000 | * |
| 465.0845_4.208 | rel_abund_log2 | post | pre | 12 | 12 | -3.508118 | 11 | 0.0049000 | ** |
| 465.2493_6.146 | rel_abund_log2 | post | pre | 12 | 12 | -2.450605 | 11 | 0.0322000 | * |
| 477.1039_4.686 | rel_abund_log2 | post | pre | 12 | 12 | -3.611319 | 11 | 0.0040900 | ** |
| 489.2123_4.076 | rel_abund_log2 | post | pre | 12 | 12 | -2.290848 | 11 | 0.0427000 | * |
| 492.1884_4.319 | rel_abund_log2 | post | pre | 12 | 12 | -2.480212 | 11 | 0.0306000 | * |
| 493.0631_3.69 | rel_abund_log2 | post | pre | 12 | 12 | -2.332401 | 11 | 0.0397000 | * |
| 493.1666_4.573 | rel_abund_log2 | post | pre | 12 | 12 | 5.069186 | 11 | 0.0003610 | *** |
| 495.2232_5.846 | rel_abund_log2 | post | pre | 12 | 12 | 3.650833 | 11 | 0.0038200 | ** |
| 497.0695_3.785 | rel_abund_log2 | post | pre | 12 | 12 | 6.593726 | 11 | 0.0000390 | **** |
| 509.0384_0.699 | rel_abund_log2 | post | pre | 12 | 12 | 8.135671 | 11 | 0.0000056 | **** |
| 509.0409_3.291 | rel_abund_log2 | post | pre | 12 | 12 | 10.822844 | 11 | 0.0000003 | **** |
| 509.2752_5.821 | rel_abund_log2 | post | pre | 12 | 12 | -2.383374 | 11 | 0.0363000 | * |
| 514.1698_4.543 | rel_abund_log2 | post | pre | 12 | 12 | -2.471245 | 11 | 0.0311000 | * |
| 521.1082_4.508 | rel_abund_log2 | post | pre | 12 | 12 | 6.703198 | 11 | 0.0000336 | **** |
| 525.0334_3.606 | rel_abund_log2 | post | pre | 12 | 12 | 10.906280 | 11 | 0.0000003 | **** |
| 528.2633_5.962 | rel_abund_log2 | post | pre | 12 | 12 | -2.743100 | 11 | 0.0191000 | * |
| 531.0773_4.532 | rel_abund_log2 | post | pre | 12 | 12 | 2.532233 | 11 | 0.0279000 | * |
| 550.1307_0.664 | rel_abund_log2 | post | pre | 12 | 12 | -2.463009 | 11 | 0.0315000 | * |
| 557.2465_7.479 | rel_abund_log2 | post | pre | 12 | 12 | 2.380755 | 11 | 0.0365000 | * |
| 572.2105_4.512 | rel_abund_log2 | post | pre | 12 | 12 | -2.587996 | 11 | 0.0252000 | * |
| 572.2528_4.339 | rel_abund_log2 | post | pre | 12 | 12 | -2.657747 | 11 | 0.0223000 | * |
| 576.1384_0.705 | rel_abund_log2 | post | pre | 12 | 12 | -3.202387 | 11 | 0.0084200 | ** |
| 579.2066_4.211 | rel_abund_log2 | post | pre | 12 | 12 | -3.081009 | 11 | 0.0105000 | * |
| 591.3184_5.021 | rel_abund_log2 | post | pre | 12 | 12 | 2.713032 | 11 | 0.0202000 | * |
| 593.3333_5.048 | rel_abund_log2 | post | pre | 12 | 12 | 2.391537 | 11 | 0.0358000 | * |
| 605.1126_3.023 | rel_abund_log2 | post | pre | 12 | 12 | 6.747767 | 11 | 0.0000317 | **** |
| 613.3589_5.89 | rel_abund_log2 | post | pre | 12 | 12 | -2.608144 | 11 | 0.0243000 | * |
| 617.1836_4.515 | rel_abund_log2 | post | pre | 12 | 12 | 5.063815 | 11 | 0.0003640 | *** |
| 639.0783_3.758 | rel_abund_log2 | post | pre | 12 | 12 | 6.004089 | 11 | 0.0000887 | **** |
| 691.3179_4.171 | rel_abund_log2 | post | pre | 12 | 12 | -2.851937 | 11 | 0.0157000 | * |
## [1] 138Red Without outlier
# run paired t-tests for control intervention
red_noOutlier_t.test_paired <- df_for_stats_noOutlier %>%
filter(Intervention == "Red") %>%
dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ pre_post,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
red_noOutlier_t.test_paired_sig <- red_noOutlier_t.test_paired %>%
filter(p <= 0.05)
kable(red_noOutlier_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1122.0248_4.125 | rel_abund_log2 | post | pre | 11 | 11 | 16.953938 | 10 | 0.0000000 | **** |
| 1122.0249_4.032 | rel_abund_log2 | post | pre | 11 | 11 | 12.864394 | 10 | 0.0000002 | **** |
| 168.0302_2.597 | rel_abund_log2 | post | pre | 11 | 11 | 2.594820 | 10 | 0.0267000 | * |
| 174.0405_0.655 | rel_abund_log2 | post | pre | 11 | 11 | -2.515647 | 10 | 0.0306000 | * |
| 187.0383_0.634 | rel_abund_log2 | post | pre | 11 | 11 | -2.567731 | 10 | 0.0280000 | * |
| 194.0458_3.657 | rel_abund_log2 | post | pre | 11 | 11 | 2.967654 | 10 | 0.0141000 | * |
| 195.051_0.627 | rel_abund_log2 | post | pre | 11 | 11 | -2.800697 | 10 | 0.0188000 | * |
| 199.0068_4.328 | rel_abund_log2 | post | pre | 11 | 11 | 2.937957 | 10 | 0.0148000 | * |
| 201.0227_4.086 | rel_abund_log2 | post | pre | 11 | 11 | 14.594623 | 10 | 0.0000000 | **** |
| 201.0228_4.555 | rel_abund_log2 | post | pre | 11 | 11 | 15.429913 | 10 | 0.0000000 | **** |
| 208.0615_3.836 | rel_abund_log2 | post | pre | 11 | 11 | -3.272724 | 10 | 0.0083900 | ** |
| 209.0532_3.773 | rel_abund_log2 | post | pre | 11 | 11 | -2.371227 | 10 | 0.0392000 | * |
| 211.0611_3.158 | rel_abund_log2 | post | pre | 11 | 11 | -3.149253 | 10 | 0.0103000 | * |
| 215.0021_3.307 | rel_abund_log2 | post | pre | 11 | 11 | 7.220693 | 10 | 0.0000286 | **** |
| 217.0172_4.4 | rel_abund_log2 | post | pre | 11 | 11 | 5.811750 | 10 | 0.0001700 | *** |
| 217.0173_3.88 | rel_abund_log2 | post | pre | 11 | 11 | 3.667624 | 10 | 0.0043300 | ** |
| 218.0821_5.22 | rel_abund_log2 | post | pre | 11 | 11 | -2.782899 | 10 | 0.0194000 | * |
| 223.0623_0.802 | rel_abund_log2 | post | pre | 11 | 11 | -2.580491 | 10 | 0.0274000 | * |
| 225.0623_0.658 | rel_abund_log2 | post | pre | 11 | 11 | -2.723898 | 10 | 0.0214000 | * |
| 227.0559_0.8 | rel_abund_log2 | post | pre | 11 | 11 | -2.268032 | 10 | 0.0467000 | * |
| 230.9967_0.757 | rel_abund_log2 | post | pre | 11 | 11 | 2.601497 | 10 | 0.0264000 | * |
| 232.9759_0.708 | rel_abund_log2 | post | pre | 11 | 11 | 3.673045 | 10 | 0.0043000 | ** |
| 235.057_0.641 | rel_abund_log2 | post | pre | 11 | 11 | -3.220758 | 10 | 0.0091600 | ** |
| 246.0753_3.721 | rel_abund_log2 | post | pre | 11 | 11 | -2.690304 | 10 | 0.0227000 | * |
| 247.0798_4.12 | rel_abund_log2 | post | pre | 11 | 11 | -2.485533 | 10 | 0.0322000 | * |
| 253.0503_5.048 | rel_abund_log2 | post | pre | 11 | 11 | 6.883945 | 10 | 0.0000428 | **** |
| 254.9816_0.664 | rel_abund_log2 | post | pre | 11 | 11 | -2.308606 | 10 | 0.0436000 | * |
| 255.0674_5.078 | rel_abund_log2 | post | pre | 11 | 11 | 3.671258 | 10 | 0.0043100 | ** |
| 257.0816_6.056 | rel_abund_log2 | post | pre | 11 | 11 | 6.505158 | 10 | 0.0000685 | **** |
| 259.057_0.712 | rel_abund_log2 | post | pre | 11 | 11 | -2.285241 | 10 | 0.0454000 | * |
| 275.0224_3.427 | rel_abund_log2 | post | pre | 11 | 11 | -2.721029 | 10 | 0.0215000 | * |
| 278.0698_0.683 | rel_abund_log2 | post | pre | 11 | 11 | -4.441324 | 10 | 0.0012500 | ** |
| 287.0526_0.66 | rel_abund_log2 | post | pre | 11 | 11 | -2.513389 | 10 | 0.0307000 | * |
| 295.0806_3.721 | rel_abund_log2 | post | pre | 11 | 11 | 2.970591 | 10 | 0.0140000 | * |
| 295.0815_4.242 | rel_abund_log2 | post | pre | 11 | 11 | 4.351551 | 10 | 0.0014400 | ** |
| 297.0975_4.072 | rel_abund_log2 | post | pre | 11 | 11 | 12.525848 | 10 | 0.0000002 | **** |
| 297.0975_4.498 | rel_abund_log2 | post | pre | 11 | 11 | 15.225047 | 10 | 0.0000000 | **** |
| 300.039_0.661 | rel_abund_log2 | post | pre | 11 | 11 | -2.945955 | 10 | 0.0146000 | * |
| 303.0712_0.728 | rel_abund_log2 | post | pre | 11 | 11 | 3.139116 | 10 | 0.0105000 | * |
| 303.072_2.684 | rel_abund_log2 | post | pre | 11 | 11 | 2.395147 | 10 | 0.0376000 | * |
| 319.0474_0.696 | rel_abund_log2 | post | pre | 11 | 11 | -2.271359 | 10 | 0.0465000 | * |
| 319.1032_0.789 | rel_abund_log2 | post | pre | 11 | 11 | -2.834109 | 10 | 0.0177000 | * |
| 328.0474_0.665 | rel_abund_log2 | post | pre | 11 | 11 | -3.051623 | 10 | 0.0122000 | * |
| 331.1218_3.932 | rel_abund_log2 | post | pre | 11 | 11 | -3.187731 | 10 | 0.0096900 | ** |
| 333.0047_4.33 | rel_abund_log2 | post | pre | 11 | 11 | 8.344941 | 10 | 0.0000081 | **** |
| 333.0066_3.893 | rel_abund_log2 | post | pre | 11 | 11 | 7.313244 | 10 | 0.0000256 | **** |
| 335.0244_4.36 | rel_abund_log2 | post | pre | 11 | 11 | 9.524045 | 10 | 0.0000025 | **** |
| 343.1396_5.368 | rel_abund_log2 | post | pre | 11 | 11 | -3.261653 | 10 | 0.0085500 | ** |
| 345.1554_3.382 | rel_abund_log2 | post | pre | 11 | 11 | -2.839922 | 10 | 0.0176000 | * |
| 345.1554_4.506 | rel_abund_log2 | post | pre | 11 | 11 | -3.450951 | 10 | 0.0062200 | ** |
| 345.1933_4.201 | rel_abund_log2 | post | pre | 11 | 11 | -2.231167 | 10 | 0.0497000 | * |
| 347.0774_4.782 | rel_abund_log2 | post | pre | 11 | 11 | 2.349123 | 10 | 0.0407000 | * |
| 347.1678_4.233 | rel_abund_log2 | post | pre | 11 | 11 | -2.418550 | 10 | 0.0362000 | * |
| 347.17_3.394 | rel_abund_log2 | post | pre | 11 | 11 | -2.243782 | 10 | 0.0487000 | * |
| 351.0199_0.667 | rel_abund_log2 | post | pre | 11 | 11 | -2.488484 | 10 | 0.0321000 | * |
| 355.1037_4.621 | rel_abund_log2 | post | pre | 11 | 11 | -4.639780 | 10 | 0.0009220 | *** |
| 359.1344_4.351 | rel_abund_log2 | post | pre | 11 | 11 | -2.922365 | 10 | 0.0152000 | * |
| 359.1347_3.481 | rel_abund_log2 | post | pre | 11 | 11 | -2.280405 | 10 | 0.0458000 | * |
| 359.1353_0.806 | rel_abund_log2 | post | pre | 11 | 11 | -3.113067 | 10 | 0.0110000 | * |
| 359.1353_3.002 | rel_abund_log2 | post | pre | 11 | 11 | -2.981756 | 10 | 0.0138000 | * |
| 361.1499_3.066 | rel_abund_log2 | post | pre | 11 | 11 | -3.010305 | 10 | 0.0131000 | * |
| 361.1502_3.49 | rel_abund_log2 | post | pre | 11 | 11 | -3.129756 | 10 | 0.0107000 | * |
| 363.021_3.933 | rel_abund_log2 | post | pre | 11 | 11 | 4.546386 | 10 | 0.0010600 | ** |
| 363.0234_4.453 | rel_abund_log2 | post | pre | 11 | 11 | 3.334866 | 10 | 0.0075600 | ** |
| 365.0863_4.095 | rel_abund_log2 | post | pre | 11 | 11 | 9.351826 | 10 | 0.0000029 | **** |
| 366.0851_4.55 | rel_abund_log2 | post | pre | 11 | 11 | 4.561034 | 10 | 0.0010400 | ** |
| 369.0829_3.476 | rel_abund_log2 | post | pre | 11 | 11 | -3.020661 | 10 | 0.0129000 | * |
| 370.0972_3.765 | rel_abund_log2 | post | pre | 11 | 11 | -3.224258 | 10 | 0.0091100 | ** |
| 370.097_3.208 | rel_abund_log2 | post | pre | 11 | 11 | -2.598766 | 10 | 0.0265000 | * |
| 371.0976_0.801 | rel_abund_log2 | post | pre | 11 | 11 | -2.580622 | 10 | 0.0274000 | * |
| 371.0978_3.481 | rel_abund_log2 | post | pre | 11 | 11 | -3.457883 | 10 | 0.0061400 | ** |
| 372.0951_0.795 | rel_abund_log2 | post | pre | 11 | 11 | -2.947224 | 10 | 0.0146000 | * |
| 373.1141_3.685 | rel_abund_log2 | post | pre | 11 | 11 | -3.336139 | 10 | 0.0075400 | ** |
| 375.1278_3.109 | rel_abund_log2 | post | pre | 11 | 11 | -2.948802 | 10 | 0.0146000 | * |
| 375.1294_0.798 | rel_abund_log2 | post | pre | 11 | 11 | -2.984270 | 10 | 0.0137000 | * |
| 393.0495_3.605 | rel_abund_log2 | post | pre | 11 | 11 | 6.844579 | 10 | 0.0000449 | **** |
| 393.0776_3.464 | rel_abund_log2 | post | pre | 11 | 11 | -2.977353 | 10 | 0.0139000 | * |
| 393.2643_6.229 | rel_abund_log2 | post | pre | 11 | 11 | -3.106032 | 10 | 0.0111000 | * |
| 397.0582_0.701 | rel_abund_log2 | post | pre | 11 | 11 | -2.977087 | 10 | 0.0139000 | * |
| 400.1429_4.669 | rel_abund_log2 | post | pre | 11 | 11 | -2.368663 | 10 | 0.0394000 | * |
| 403.1133_4.514 | rel_abund_log2 | post | pre | 11 | 11 | -2.832928 | 10 | 0.0178000 | * |
| 405.0469_0.697 | rel_abund_log2 | post | pre | 11 | 11 | -2.395438 | 10 | 0.0376000 | * |
| 405.118_5.456 | rel_abund_log2 | post | pre | 11 | 11 | -2.597200 | 10 | 0.0266000 | * |
| 407.0405_4.991 | rel_abund_log2 | post | pre | 11 | 11 | -2.314706 | 10 | 0.0432000 | * |
| 411.2021_4.884 | rel_abund_log2 | post | pre | 11 | 11 | -2.371909 | 10 | 0.0391000 | * |
| 413.1426_4.219 | rel_abund_log2 | post | pre | 11 | 11 | -3.495668 | 10 | 0.0057700 | ** |
| 413.1426_4.51 | rel_abund_log2 | post | pre | 11 | 11 | -3.881706 | 10 | 0.0030500 | ** |
| 413.1436_3.2 | rel_abund_log2 | post | pre | 11 | 11 | -2.273782 | 10 | 0.0463000 | * |
| 415.1607_0.797 | rel_abund_log2 | post | pre | 11 | 11 | -2.295768 | 10 | 0.0446000 | * |
| 418.1149_0.722 | rel_abund_log2 | post | pre | 11 | 11 | -3.864202 | 10 | 0.0031400 | ** |
| 427.197_3.64 | rel_abund_log2 | post | pre | 11 | 11 | -2.915237 | 10 | 0.0154000 | * |
| 427.197_4.049 | rel_abund_log2 | post | pre | 11 | 11 | -3.079159 | 10 | 0.0117000 | * |
| 429.0815_4.032 | rel_abund_log2 | post | pre | 11 | 11 | 10.579620 | 10 | 0.0000009 | **** |
| 429.082_3.756 | rel_abund_log2 | post | pre | 11 | 11 | 11.197913 | 10 | 0.0000006 | **** |
| 429.083_3.201 | rel_abund_log2 | post | pre | 11 | 11 | 10.376468 | 10 | 0.0000011 | **** |
| 429.1762_5.798 | rel_abund_log2 | post | pre | 11 | 11 | 2.506597 | 10 | 0.0311000 | * |
| 431.0534_0.666 | rel_abund_log2 | post | pre | 11 | 11 | -2.827706 | 10 | 0.0179000 | * |
| 431.0979_3.907 | rel_abund_log2 | post | pre | 11 | 11 | 10.555630 | 10 | 0.0000010 | **** |
| 433.0646_4.045 | rel_abund_log2 | post | pre | 11 | 11 | 4.483248 | 10 | 0.0011700 | ** |
| 433.1136_4.519 | rel_abund_log2 | post | pre | 11 | 11 | 4.775541 | 10 | 0.0007510 | *** |
| 433.113_4.886 | rel_abund_log2 | post | pre | 11 | 11 | 10.832767 | 10 | 0.0000008 | **** |
| 434.1396_3.922 | rel_abund_log2 | post | pre | 11 | 11 | -2.584976 | 10 | 0.0272000 | * |
| 437.0526_5.832 | rel_abund_log2 | post | pre | 11 | 11 | 2.560461 | 10 | 0.0284000 | * |
| 445.0782_4.484 | rel_abund_log2 | post | pre | 11 | 11 | 7.392075 | 10 | 0.0000234 | **** |
| 447.0919_4.504 | rel_abund_log2 | post | pre | 11 | 11 | 2.835809 | 10 | 0.0177000 | * |
| 447.0921_4.067 | rel_abund_log2 | post | pre | 11 | 11 | 2.895355 | 10 | 0.0160000 | * |
| 447.0928_3.777 | rel_abund_log2 | post | pre | 11 | 11 | 5.862940 | 10 | 0.0001590 | *** |
| 447.092_4.187 | rel_abund_log2 | post | pre | 11 | 11 | 3.286366 | 10 | 0.0082000 | ** |
| 449.0698_2.589 | rel_abund_log2 | post | pre | 11 | 11 | 2.722287 | 10 | 0.0215000 | * |
| 449.1074_4.301 | rel_abund_log2 | post | pre | 11 | 11 | 5.717157 | 10 | 0.0001940 | *** |
| 449.254_8.044 | rel_abund_log2 | post | pre | 11 | 11 | -2.516932 | 10 | 0.0305000 | * |
| 459.0843_2.964 | rel_abund_log2 | post | pre | 11 | 11 | 2.440971 | 10 | 0.0348000 | * |
| 459.093_3.801 | rel_abund_log2 | post | pre | 11 | 11 | 15.975248 | 10 | 0.0000000 | **** |
| 463.2334_5.897 | rel_abund_log2 | post | pre | 11 | 11 | -2.964803 | 10 | 0.0142000 | * |
| 465.0845_4.208 | rel_abund_log2 | post | pre | 11 | 11 | -3.506125 | 10 | 0.0056700 | ** |
| 465.2493_6.146 | rel_abund_log2 | post | pre | 11 | 11 | -2.362648 | 10 | 0.0398000 | * |
| 475.0782_0.667 | rel_abund_log2 | post | pre | 11 | 11 | -2.477009 | 10 | 0.0327000 | * |
| 477.1039_4.686 | rel_abund_log2 | post | pre | 11 | 11 | -4.364232 | 10 | 0.0014100 | ** |
| 492.1884_4.319 | rel_abund_log2 | post | pre | 11 | 11 | -2.491375 | 10 | 0.0319000 | * |
| 493.0631_3.69 | rel_abund_log2 | post | pre | 11 | 11 | -2.305521 | 10 | 0.0438000 | * |
| 493.1666_4.573 | rel_abund_log2 | post | pre | 11 | 11 | 4.823781 | 10 | 0.0006980 | *** |
| 495.2232_5.846 | rel_abund_log2 | post | pre | 11 | 11 | 3.638677 | 10 | 0.0045500 | ** |
| 497.0695_3.785 | rel_abund_log2 | post | pre | 11 | 11 | 7.612692 | 10 | 0.0000181 | **** |
| 501.2122_5.698 | rel_abund_log2 | post | pre | 11 | 11 | -2.539997 | 10 | 0.0294000 | * |
| 509.0384_0.699 | rel_abund_log2 | post | pre | 11 | 11 | 8.465811 | 10 | 0.0000072 | **** |
| 509.0409_3.291 | rel_abund_log2 | post | pre | 11 | 11 | 13.572124 | 10 | 0.0000001 | **** |
| 509.2752_5.821 | rel_abund_log2 | post | pre | 11 | 11 | -2.359686 | 10 | 0.0400000 | * |
| 521.1082_4.508 | rel_abund_log2 | post | pre | 11 | 11 | 6.174921 | 10 | 0.0001050 | *** |
| 525.0334_3.606 | rel_abund_log2 | post | pre | 11 | 11 | 10.488811 | 10 | 0.0000010 | **** |
| 528.2633_5.962 | rel_abund_log2 | post | pre | 11 | 11 | -2.348740 | 10 | 0.0407000 | * |
| 550.1307_0.664 | rel_abund_log2 | post | pre | 11 | 11 | -2.372318 | 10 | 0.0391000 | * |
| 572.2105_4.512 | rel_abund_log2 | post | pre | 11 | 11 | -2.527125 | 10 | 0.0300000 | * |
| 572.2528_4.339 | rel_abund_log2 | post | pre | 11 | 11 | -2.731114 | 10 | 0.0212000 | * |
| 576.1384_0.705 | rel_abund_log2 | post | pre | 11 | 11 | -4.248668 | 10 | 0.0016900 | ** |
| 579.2066_4.211 | rel_abund_log2 | post | pre | 11 | 11 | -2.663537 | 10 | 0.0238000 | * |
| 591.3184_5.021 | rel_abund_log2 | post | pre | 11 | 11 | 2.786554 | 10 | 0.0192000 | * |
| 593.3333_5.048 | rel_abund_log2 | post | pre | 11 | 11 | 3.403899 | 10 | 0.0067300 | ** |
| 605.1126_3.023 | rel_abund_log2 | post | pre | 11 | 11 | 7.581455 | 10 | 0.0000188 | **** |
| 613.3589_5.89 | rel_abund_log2 | post | pre | 11 | 11 | -2.541285 | 10 | 0.0293000 | * |
| 617.1836_4.515 | rel_abund_log2 | post | pre | 11 | 11 | 4.577883 | 10 | 0.0010100 | ** |
| 639.0783_3.758 | rel_abund_log2 | post | pre | 11 | 11 | 7.448942 | 10 | 0.0000219 | **** |
| 691.3179_4.171 | rel_abund_log2 | post | pre | 11 | 11 | -2.733884 | 10 | 0.0211000 | * |
## [1] 142Post-red vs post-yellow
# run paired t-tests for post interventions
post_t.test_paired <- df_for_stats %>%
filter(pre_post == "post") %>%
dplyr::select(Subject, Intervention, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ Intervention,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
post_t.test_paired_sig <- post_t.test_paired %>%
filter(p <= 0.05)
kable(post_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1122.0248_4.125 | rel_abund_log2 | Red | Yellow | 12 | 12 | 12.261341 | 11 | 0.0000001 | **** |
| 1122.0249_4.032 | rel_abund_log2 | Red | Yellow | 12 | 12 | 14.335475 | 11 | 0.0000000 | **** |
| 175.0241_0.753 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.652839 | 11 | 0.0225000 | * |
| 178.0524_2.721 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.881217 | 11 | 0.0149000 | * |
| 201.0227_4.086 | rel_abund_log2 | Red | Yellow | 12 | 12 | 16.654418 | 11 | 0.0000000 | **** |
| 201.0228_4.555 | rel_abund_log2 | Red | Yellow | 12 | 12 | 19.108616 | 11 | 0.0000000 | **** |
| 206.9967_2.418 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.768641 | 11 | 0.0183000 | * |
| 215.0021_3.307 | rel_abund_log2 | Red | Yellow | 12 | 12 | 3.755225 | 11 | 0.0031800 | ** |
| 217.0172_4.4 | rel_abund_log2 | Red | Yellow | 12 | 12 | 4.244663 | 11 | 0.0013800 | ** |
| 217.0173_3.88 | rel_abund_log2 | Red | Yellow | 12 | 12 | 2.605892 | 11 | 0.0244000 | * |
| 253.0503_5.048 | rel_abund_log2 | Red | Yellow | 12 | 12 | 11.662893 | 11 | 0.0000002 | **** |
| 255.0674_5.078 | rel_abund_log2 | Red | Yellow | 12 | 12 | 3.956741 | 11 | 0.0022500 | ** |
| 257.0816_6.056 | rel_abund_log2 | Red | Yellow | 12 | 12 | 7.824727 | 11 | 0.0000081 | **** |
| 297.0975_4.072 | rel_abund_log2 | Red | Yellow | 12 | 12 | 11.856212 | 11 | 0.0000001 | **** |
| 297.0975_4.498 | rel_abund_log2 | Red | Yellow | 12 | 12 | 15.659551 | 11 | 0.0000000 | **** |
| 333.0047_4.33 | rel_abund_log2 | Red | Yellow | 12 | 12 | 8.894883 | 11 | 0.0000024 | **** |
| 333.0066_3.893 | rel_abund_log2 | Red | Yellow | 12 | 12 | 8.212012 | 11 | 0.0000051 | **** |
| 335.0244_4.36 | rel_abund_log2 | Red | Yellow | 12 | 12 | 11.295074 | 11 | 0.0000002 | **** |
| 345.1554_4.506 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.357006 | 11 | 0.0380000 | * |
| 345.1933_4.201 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.646888 | 11 | 0.0227000 | * |
| 361.1499_3.066 | rel_abund_log2 | Red | Yellow | 12 | 12 | -3.290661 | 11 | 0.0072000 | ** |
| 363.021_3.933 | rel_abund_log2 | Red | Yellow | 12 | 12 | 4.252842 | 11 | 0.0013600 | ** |
| 363.0234_4.453 | rel_abund_log2 | Red | Yellow | 12 | 12 | 4.151299 | 11 | 0.0016100 | ** |
| 365.0863_4.095 | rel_abund_log2 | Red | Yellow | 12 | 12 | 8.089468 | 11 | 0.0000059 | **** |
| 366.0851_4.55 | rel_abund_log2 | Red | Yellow | 12 | 12 | 4.171253 | 11 | 0.0015600 | ** |
| 369.0829_3.476 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.352103 | 11 | 0.0383000 | * |
| 372.0951_0.795 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.209135 | 11 | 0.0493000 | * |
| 393.0495_3.605 | rel_abund_log2 | Red | Yellow | 12 | 12 | 5.412055 | 11 | 0.0002130 | *** |
| 413.1426_4.51 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.486714 | 11 | 0.0302000 | * |
| 427.197_3.64 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.598312 | 11 | 0.0248000 | * |
| 427.197_4.049 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.223249 | 11 | 0.0481000 | * |
| 429.0815_4.032 | rel_abund_log2 | Red | Yellow | 12 | 12 | 16.032414 | 11 | 0.0000000 | **** |
| 429.082_3.756 | rel_abund_log2 | Red | Yellow | 12 | 12 | 10.730400 | 11 | 0.0000004 | **** |
| 429.083_3.201 | rel_abund_log2 | Red | Yellow | 12 | 12 | 9.736330 | 11 | 0.0000010 | **** |
| 431.0979_3.907 | rel_abund_log2 | Red | Yellow | 12 | 12 | 10.232249 | 11 | 0.0000006 | **** |
| 433.0646_4.045 | rel_abund_log2 | Red | Yellow | 12 | 12 | 3.374245 | 11 | 0.0062100 | ** |
| 433.1136_4.519 | rel_abund_log2 | Red | Yellow | 12 | 12 | 5.160475 | 11 | 0.0003130 | *** |
| 433.113_4.886 | rel_abund_log2 | Red | Yellow | 12 | 12 | 17.881262 | 11 | 0.0000000 | **** |
| 445.0782_4.484 | rel_abund_log2 | Red | Yellow | 12 | 12 | 7.998582 | 11 | 0.0000065 | **** |
| 447.0928_3.777 | rel_abund_log2 | Red | Yellow | 12 | 12 | 2.889559 | 11 | 0.0147000 | * |
| 447.092_4.187 | rel_abund_log2 | Red | Yellow | 12 | 12 | 2.431750 | 11 | 0.0333000 | * |
| 449.1074_4.301 | rel_abund_log2 | Red | Yellow | 12 | 12 | 4.555092 | 11 | 0.0008230 | *** |
| 459.093_3.801 | rel_abund_log2 | Red | Yellow | 12 | 12 | 16.698655 | 11 | 0.0000000 | **** |
| 493.1666_4.573 | rel_abund_log2 | Red | Yellow | 12 | 12 | 5.699684 | 11 | 0.0001380 | *** |
| 495.2232_5.846 | rel_abund_log2 | Red | Yellow | 12 | 12 | 2.683297 | 11 | 0.0213000 | * |
| 497.0695_3.785 | rel_abund_log2 | Red | Yellow | 12 | 12 | 7.921694 | 11 | 0.0000072 | **** |
| 509.0384_0.699 | rel_abund_log2 | Red | Yellow | 12 | 12 | 7.903464 | 11 | 0.0000073 | **** |
| 509.0409_3.291 | rel_abund_log2 | Red | Yellow | 12 | 12 | 8.612345 | 11 | 0.0000032 | **** |
| 521.1082_4.508 | rel_abund_log2 | Red | Yellow | 12 | 12 | 6.326623 | 11 | 0.0000562 | **** |
| 525.0334_3.606 | rel_abund_log2 | Red | Yellow | 12 | 12 | 7.686252 | 11 | 0.0000095 | **** |
| 531.0773_4.532 | rel_abund_log2 | Red | Yellow | 12 | 12 | 2.438338 | 11 | 0.0329000 | * |
| 572.2105_4.512 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.836010 | 11 | 0.0162000 | * |
| 576.1384_0.705 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.364335 | 11 | 0.0375000 | * |
| 605.1126_3.023 | rel_abund_log2 | Red | Yellow | 12 | 12 | 6.568304 | 11 | 0.0000403 | **** |
| 617.1836_4.515 | rel_abund_log2 | Red | Yellow | 12 | 12 | 4.771226 | 11 | 0.0005800 | *** |
| 639.0783_3.758 | rel_abund_log2 | Red | Yellow | 12 | 12 | 7.150782 | 11 | 0.0000187 | **** |
| 691.3179_4.171 | rel_abund_log2 | Red | Yellow | 12 | 12 | -2.779515 | 11 | 0.0179000 | * |
## [1] 57Post-red vs post-yellow Without Outlier
# run paired t-tests for post interventions
post_noOutlier_t.test_paired <- df_for_stats %>%
filter(pre_post == "post",
Subject != "6106") %>%
dplyr::select(Subject, Intervention, mz_rt, rel_abund_log2) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ Intervention,
paired = TRUE,
p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
add_significance()Statistically significant features
# which features are significant?
post_noOutlier_t.test_paired_sig <- post_noOutlier_t.test_paired %>%
filter(p <= 0.05)
kable(post_noOutlier_t.test_paired_sig)| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1122.0248_4.125 | rel_abund_log2 | Red | Yellow | 11 | 11 | 16.568040 | 10 | 0.0000000 | **** |
| 1122.0249_4.032 | rel_abund_log2 | Red | Yellow | 11 | 11 | 17.935274 | 10 | 0.0000000 | **** |
| 175.0241_0.753 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.426977 | 10 | 0.0356000 | * |
| 178.0524_2.721 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.604367 | 10 | 0.0263000 | * |
| 201.0227_4.086 | rel_abund_log2 | Red | Yellow | 11 | 11 | 18.752278 | 10 | 0.0000000 | **** |
| 201.0228_4.555 | rel_abund_log2 | Red | Yellow | 11 | 11 | 20.576006 | 10 | 0.0000000 | **** |
| 206.9967_2.418 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.516755 | 10 | 0.0306000 | * |
| 217.0172_4.4 | rel_abund_log2 | Red | Yellow | 11 | 11 | 4.679864 | 10 | 0.0008680 | *** |
| 217.0173_3.88 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.291675 | 10 | 0.0449000 | * |
| 241.1189_3.536 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.359264 | 10 | 0.0400000 | * |
| 243.1349_3.353 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.296458 | 10 | 0.0445000 | * |
| 253.0503_5.048 | rel_abund_log2 | Red | Yellow | 11 | 11 | 10.636744 | 10 | 0.0000009 | **** |
| 255.0674_5.078 | rel_abund_log2 | Red | Yellow | 11 | 11 | 3.816934 | 10 | 0.0033900 | ** |
| 257.0816_6.056 | rel_abund_log2 | Red | Yellow | 11 | 11 | 7.084909 | 10 | 0.0000336 | **** |
| 261.039_0.66 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.304029 | 10 | 0.0440000 | * |
| 297.0975_4.072 | rel_abund_log2 | Red | Yellow | 11 | 11 | 10.343403 | 10 | 0.0000012 | **** |
| 297.0975_4.498 | rel_abund_log2 | Red | Yellow | 11 | 11 | 13.921226 | 10 | 0.0000001 | **** |
| 333.0047_4.33 | rel_abund_log2 | Red | Yellow | 11 | 11 | 9.840711 | 10 | 0.0000018 | **** |
| 333.0066_3.893 | rel_abund_log2 | Red | Yellow | 11 | 11 | 10.103779 | 10 | 0.0000015 | **** |
| 335.0244_4.36 | rel_abund_log2 | Red | Yellow | 11 | 11 | 11.248800 | 10 | 0.0000005 | **** |
| 345.1554_4.506 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.481438 | 10 | 0.0325000 | * |
| 345.1933_4.201 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.488524 | 10 | 0.0321000 | * |
| 359.1353_0.806 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.967480 | 10 | 0.0141000 | * |
| 361.1499_3.066 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.509862 | 10 | 0.0309000 | * |
| 363.021_3.933 | rel_abund_log2 | Red | Yellow | 11 | 11 | 3.950894 | 10 | 0.0027300 | ** |
| 363.0234_4.453 | rel_abund_log2 | Red | Yellow | 11 | 11 | 3.098551 | 10 | 0.0113000 | * |
| 365.0863_4.095 | rel_abund_log2 | Red | Yellow | 11 | 11 | 8.889569 | 10 | 0.0000046 | **** |
| 366.0851_4.55 | rel_abund_log2 | Red | Yellow | 11 | 11 | 4.001611 | 10 | 0.0025100 | ** |
| 369.0829_3.476 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.431822 | 10 | 0.0353000 | * |
| 373.1141_3.685 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.930087 | 10 | 0.0150000 | * |
| 393.0495_3.605 | rel_abund_log2 | Red | Yellow | 11 | 11 | 6.076526 | 10 | 0.0001190 | *** |
| 405.0469_0.697 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.549860 | 10 | 0.0289000 | * |
| 413.1426_4.219 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.683601 | 10 | 0.0230000 | * |
| 413.1426_4.51 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.673485 | 10 | 0.0234000 | * |
| 413.1436_3.2 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.235340 | 10 | 0.0494000 | * |
| 427.1967_4.894 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.313638 | 10 | 0.0432000 | * |
| 427.197_3.64 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.619251 | 10 | 0.0256000 | * |
| 427.197_4.049 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.382279 | 10 | 0.0385000 | * |
| 429.0815_4.032 | rel_abund_log2 | Red | Yellow | 11 | 11 | 14.300935 | 10 | 0.0000001 | **** |
| 429.082_3.756 | rel_abund_log2 | Red | Yellow | 11 | 11 | 13.732174 | 10 | 0.0000001 | **** |
| 429.083_3.201 | rel_abund_log2 | Red | Yellow | 11 | 11 | 11.870765 | 10 | 0.0000003 | **** |
| 431.0979_3.907 | rel_abund_log2 | Red | Yellow | 11 | 11 | 9.146268 | 10 | 0.0000036 | **** |
| 433.0646_4.045 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.550534 | 10 | 0.0288000 | * |
| 433.1136_4.519 | rel_abund_log2 | Red | Yellow | 11 | 11 | 4.442153 | 10 | 0.0012500 | ** |
| 433.113_4.886 | rel_abund_log2 | Red | Yellow | 11 | 11 | 14.223055 | 10 | 0.0000001 | **** |
| 445.0782_4.484 | rel_abund_log2 | Red | Yellow | 11 | 11 | 6.830648 | 10 | 0.0000457 | **** |
| 447.0928_3.777 | rel_abund_log2 | Red | Yellow | 11 | 11 | 3.852655 | 10 | 0.0032000 | ** |
| 447.092_4.187 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.517924 | 10 | 0.0305000 | * |
| 449.1074_4.301 | rel_abund_log2 | Red | Yellow | 11 | 11 | 3.914249 | 10 | 0.0028900 | ** |
| 459.093_3.801 | rel_abund_log2 | Red | Yellow | 11 | 11 | 17.666591 | 10 | 0.0000000 | **** |
| 493.0631_3.69 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.349189 | 10 | 0.0407000 | * |
| 493.1666_4.573 | rel_abund_log2 | Red | Yellow | 11 | 11 | 6.227474 | 10 | 0.0000979 | **** |
| 497.0695_3.785 | rel_abund_log2 | Red | Yellow | 11 | 11 | 13.235046 | 10 | 0.0000001 | **** |
| 509.0384_0.699 | rel_abund_log2 | Red | Yellow | 11 | 11 | 8.337022 | 10 | 0.0000082 | **** |
| 509.0409_3.291 | rel_abund_log2 | Red | Yellow | 11 | 11 | 10.024150 | 10 | 0.0000016 | **** |
| 521.1082_4.508 | rel_abund_log2 | Red | Yellow | 11 | 11 | 6.840537 | 10 | 0.0000451 | **** |
| 525.0334_3.606 | rel_abund_log2 | Red | Yellow | 11 | 11 | 10.067446 | 10 | 0.0000015 | **** |
| 572.2105_4.512 | rel_abund_log2 | Red | Yellow | 11 | 11 | -3.073439 | 10 | 0.0118000 | * |
| 576.1384_0.705 | rel_abund_log2 | Red | Yellow | 11 | 11 | -3.123052 | 10 | 0.0108000 | * |
| 605.1126_3.023 | rel_abund_log2 | Red | Yellow | 11 | 11 | 7.143170 | 10 | 0.0000313 | **** |
| 617.1836_4.515 | rel_abund_log2 | Red | Yellow | 11 | 11 | 4.484883 | 10 | 0.0011700 | ** |
| 639.0783_3.758 | rel_abund_log2 | Red | Yellow | 11 | 11 | 8.315119 | 10 | 0.0000084 | **** |
| 681.2357_4.115 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.242285 | 10 | 0.0488000 | * |
| 691.3179_4.171 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.587562 | 10 | 0.0271000 | * |
## [1] 64Mummichog export
Outlier comparison
Are there any significant features shared between tests with and without outlier?
# return sig features present only in df with outlier, and not in df without outlier
kable(anti_join(post_noOutlier_t.test_paired_sig,
post_t.test_paired_sig,
by = "mz_rt"))| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 241.1189_3.536 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.359264 | 10 | 0.0400 | * |
| 243.1349_3.353 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.296458 | 10 | 0.0445 | * |
| 261.039_0.66 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.304029 | 10 | 0.0440 | * |
| 359.1353_0.806 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.967480 | 10 | 0.0141 | * |
| 373.1141_3.685 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.930087 | 10 | 0.0150 | * |
| 405.0469_0.697 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.549860 | 10 | 0.0289 | * |
| 413.1426_4.219 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.683601 | 10 | 0.0230 | * |
| 413.1436_3.2 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.235340 | 10 | 0.0494 | * |
| 427.1967_4.894 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.313638 | 10 | 0.0432 | * |
| 493.0631_3.69 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.349189 | 10 | 0.0407 | * |
| 681.2357_4.115 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.242285 | 10 | 0.0488 | * |
## [1] 11# return sig features from df with no outlier that are also present in df with outlier
kable(semi_join(post_noOutlier_t.test_paired_sig,
post_t.test_paired_sig,
by = "mz_rt"))| mz_rt | .y. | group1 | group2 | n1 | n2 | statistic | df | p | p.signif |
|---|---|---|---|---|---|---|---|---|---|
| 1122.0248_4.125 | rel_abund_log2 | Red | Yellow | 11 | 11 | 16.568040 | 10 | 0.0000000 | **** |
| 1122.0249_4.032 | rel_abund_log2 | Red | Yellow | 11 | 11 | 17.935274 | 10 | 0.0000000 | **** |
| 175.0241_0.753 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.426977 | 10 | 0.0356000 | * |
| 178.0524_2.721 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.604367 | 10 | 0.0263000 | * |
| 201.0227_4.086 | rel_abund_log2 | Red | Yellow | 11 | 11 | 18.752278 | 10 | 0.0000000 | **** |
| 201.0228_4.555 | rel_abund_log2 | Red | Yellow | 11 | 11 | 20.576006 | 10 | 0.0000000 | **** |
| 206.9967_2.418 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.516755 | 10 | 0.0306000 | * |
| 217.0172_4.4 | rel_abund_log2 | Red | Yellow | 11 | 11 | 4.679864 | 10 | 0.0008680 | *** |
| 217.0173_3.88 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.291675 | 10 | 0.0449000 | * |
| 253.0503_5.048 | rel_abund_log2 | Red | Yellow | 11 | 11 | 10.636744 | 10 | 0.0000009 | **** |
| 255.0674_5.078 | rel_abund_log2 | Red | Yellow | 11 | 11 | 3.816934 | 10 | 0.0033900 | ** |
| 257.0816_6.056 | rel_abund_log2 | Red | Yellow | 11 | 11 | 7.084909 | 10 | 0.0000336 | **** |
| 297.0975_4.072 | rel_abund_log2 | Red | Yellow | 11 | 11 | 10.343403 | 10 | 0.0000012 | **** |
| 297.0975_4.498 | rel_abund_log2 | Red | Yellow | 11 | 11 | 13.921226 | 10 | 0.0000001 | **** |
| 333.0047_4.33 | rel_abund_log2 | Red | Yellow | 11 | 11 | 9.840711 | 10 | 0.0000018 | **** |
| 333.0066_3.893 | rel_abund_log2 | Red | Yellow | 11 | 11 | 10.103779 | 10 | 0.0000015 | **** |
| 335.0244_4.36 | rel_abund_log2 | Red | Yellow | 11 | 11 | 11.248800 | 10 | 0.0000005 | **** |
| 345.1554_4.506 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.481438 | 10 | 0.0325000 | * |
| 345.1933_4.201 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.488524 | 10 | 0.0321000 | * |
| 361.1499_3.066 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.509862 | 10 | 0.0309000 | * |
| 363.021_3.933 | rel_abund_log2 | Red | Yellow | 11 | 11 | 3.950894 | 10 | 0.0027300 | ** |
| 363.0234_4.453 | rel_abund_log2 | Red | Yellow | 11 | 11 | 3.098551 | 10 | 0.0113000 | * |
| 365.0863_4.095 | rel_abund_log2 | Red | Yellow | 11 | 11 | 8.889569 | 10 | 0.0000046 | **** |
| 366.0851_4.55 | rel_abund_log2 | Red | Yellow | 11 | 11 | 4.001611 | 10 | 0.0025100 | ** |
| 369.0829_3.476 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.431822 | 10 | 0.0353000 | * |
| 393.0495_3.605 | rel_abund_log2 | Red | Yellow | 11 | 11 | 6.076526 | 10 | 0.0001190 | *** |
| 413.1426_4.51 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.673485 | 10 | 0.0234000 | * |
| 427.197_3.64 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.619251 | 10 | 0.0256000 | * |
| 427.197_4.049 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.382279 | 10 | 0.0385000 | * |
| 429.0815_4.032 | rel_abund_log2 | Red | Yellow | 11 | 11 | 14.300935 | 10 | 0.0000001 | **** |
| 429.082_3.756 | rel_abund_log2 | Red | Yellow | 11 | 11 | 13.732174 | 10 | 0.0000001 | **** |
| 429.083_3.201 | rel_abund_log2 | Red | Yellow | 11 | 11 | 11.870765 | 10 | 0.0000003 | **** |
| 431.0979_3.907 | rel_abund_log2 | Red | Yellow | 11 | 11 | 9.146268 | 10 | 0.0000036 | **** |
| 433.0646_4.045 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.550534 | 10 | 0.0288000 | * |
| 433.1136_4.519 | rel_abund_log2 | Red | Yellow | 11 | 11 | 4.442153 | 10 | 0.0012500 | ** |
| 433.113_4.886 | rel_abund_log2 | Red | Yellow | 11 | 11 | 14.223055 | 10 | 0.0000001 | **** |
| 445.0782_4.484 | rel_abund_log2 | Red | Yellow | 11 | 11 | 6.830648 | 10 | 0.0000457 | **** |
| 447.0928_3.777 | rel_abund_log2 | Red | Yellow | 11 | 11 | 3.852655 | 10 | 0.0032000 | ** |
| 447.092_4.187 | rel_abund_log2 | Red | Yellow | 11 | 11 | 2.517924 | 10 | 0.0305000 | * |
| 449.1074_4.301 | rel_abund_log2 | Red | Yellow | 11 | 11 | 3.914249 | 10 | 0.0028900 | ** |
| 459.093_3.801 | rel_abund_log2 | Red | Yellow | 11 | 11 | 17.666591 | 10 | 0.0000000 | **** |
| 493.1666_4.573 | rel_abund_log2 | Red | Yellow | 11 | 11 | 6.227474 | 10 | 0.0000979 | **** |
| 497.0695_3.785 | rel_abund_log2 | Red | Yellow | 11 | 11 | 13.235046 | 10 | 0.0000001 | **** |
| 509.0384_0.699 | rel_abund_log2 | Red | Yellow | 11 | 11 | 8.337022 | 10 | 0.0000082 | **** |
| 509.0409_3.291 | rel_abund_log2 | Red | Yellow | 11 | 11 | 10.024150 | 10 | 0.0000016 | **** |
| 521.1082_4.508 | rel_abund_log2 | Red | Yellow | 11 | 11 | 6.840537 | 10 | 0.0000451 | **** |
| 525.0334_3.606 | rel_abund_log2 | Red | Yellow | 11 | 11 | 10.067446 | 10 | 0.0000015 | **** |
| 572.2105_4.512 | rel_abund_log2 | Red | Yellow | 11 | 11 | -3.073439 | 10 | 0.0118000 | * |
| 576.1384_0.705 | rel_abund_log2 | Red | Yellow | 11 | 11 | -3.123052 | 10 | 0.0108000 | * |
| 605.1126_3.023 | rel_abund_log2 | Red | Yellow | 11 | 11 | 7.143170 | 10 | 0.0000313 | **** |
| 617.1836_4.515 | rel_abund_log2 | Red | Yellow | 11 | 11 | 4.484883 | 10 | 0.0011700 | ** |
| 639.0783_3.758 | rel_abund_log2 | Red | Yellow | 11 | 11 | 8.315119 | 10 | 0.0000084 | **** |
| 691.3179_4.171 | rel_abund_log2 | Red | Yellow | 11 | 11 | -2.587562 | 10 | 0.0271000 | * |
## [1] 53Standard comparisons
Here, I want to only focus on the metabolites that I hypothesized to change. This way I can avoid multiple correction adjustments and see if I can capture any significant differences at different timepoints
Wrangle
stds_df_for_stats_wide <- df_for_stats %>%
dplyr::select(!rel_abund) %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2) %>%
dplyr::select(c(1:11),
"178.0515_3.479", #hippuric acid
"179.0556_0.643", #glucose
"154.0621_0.609", #histidine
"191.0203_0.697", #citric acid
"212.0027_3.304", #indoxyl sulfate
"253.0503_5.048", #daidzein
"257.0816_6.056" #O-DMA
)
# make tidy df
stds_df_forstats_tidy <- stds_df_for_stats_wide %>%
pivot_longer(cols = 12:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund_log2")# changing factor levels for pre_post_intervention
stds_df_forstats_tidy$pre_post_intervention <- factor(stds_df_forstats_tidy$pre_post_intervention,
levels = c("pre_Yellow", "post_Yellow", "pre_Red", "post_Red"))
levels(stds_df_forstats_tidy$pre_post_intervention) ## [1] "pre_Yellow" "post_Yellow" "pre_Red" "post_Red"Boxplots
stds_df_forstats_tidy %>%
ggplot(aes(x = pre_post_intervention, y = rel_abund_log2, fill = Intervention)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = c("Yellow" = "gold",
"Red" = "tomato1")) +
geom_line(aes(group = Subject), colour = "purple", linewidth = 0.2) +
facet_wrap(vars(mz_rt), scales = "free_y") +
theme_bw() 
Control t-test
# run paired t-tests for before vs. aftet control intervention
stds_ctrl_t.test_paired <- stds_df_forstats_tidy %>%
filter(Intervention == "Yellow") %>%
dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
filter(Subject != 6106) %>% # remove outliers
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ pre_post,
paired = TRUE) %>%
add_significance()Statistically significant features
# which features are significant?
stds_ctrl_t.test_paired_sig <- stds_ctrl_t.test_paired %>%
filter(p <= 0.05)
tibble(stds_ctrl_t.test_paired_sig)## # A tibble: 1 × 10
## mz_rt .y. group1 group2 n1 n2 statistic df p p.signif
## <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 154.0621_0.6… rel_… post pre 11 11 -3.54 10 0.00533 **## [1] 1Red t-tests
# run paired t-tests for before vs. after red intervention
stds_red_t.test_paired <- stds_df_forstats_tidy %>%
filter(Intervention == "Red") %>%
dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
filter(Subject != 6106) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ pre_post,
paired = TRUE) %>%
add_significance()Statistically significant features
# which features are significant?
stds_red_t.test_paired_sig <- stds_red_t.test_paired %>%
filter(p <= 0.05)
tibble(stds_red_t.test_paired_sig)## # A tibble: 2 × 10
## mz_rt .y. group1 group2 n1 n2 statistic df p p.signif
## <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 253.0503_5.0… rel_… post pre 11 11 6.88 10 4.28e-5 ****
## 2 257.0816_6.0… rel_… post pre 11 11 6.51 10 6.85e-5 ****## [1] 2Intervention t-tests
# run paired t-tests for post-red vs. post-control intervention
stds_post_t.test_paired <- stds_df_forstats_tidy %>%
filter(pre_post == "post") %>%
dplyr::select(Subject, pre_post_intervention, mz_rt, rel_abund_log2) %>%
filter(Subject != 6106) %>%
group_by(mz_rt) %>%
t_test(rel_abund_log2 ~ pre_post_intervention,
paired = TRUE) %>%
add_significance()Statistically significant features
# which features are significant?
stds_post_t.test_paired_sig <- stds_post_t.test_paired %>%
filter(p <= 0.05)
tibble(stds_post_t.test_paired_sig)## # A tibble: 2 × 10
## mz_rt .y. group1 group2 n1 n2 statistic df p p.signif
## <chr> <chr> <chr> <chr> <int> <int> <dbl> <dbl> <dbl> <chr>
## 1 253.0503_5.0… rel_… post_… post_… 11 11 -10.6 10 9 e-7 ****
## 2 257.0816_6.0… rel_… post_… post_… 11 11 -7.08 10 3.36e-5 ****## [1] 2Volcano plots
Post-intervention comparisons
Wrangle (no outlier)
# calculate mean rel abund (not log) by sample, and avg fold change (FC) diff by feature
red_v_yel_volcano_data_noOutlier <- df_for_stats %>%
filter(pre_post == "post",
Subject != 6106) %>% # remove outlier subj
group_by(Intervention, mz_rt) %>%
summarize(mean_rel_abund = mean(rel_abund)) %>%
pivot_wider(names_from = Intervention, values_from = mean_rel_abund) %>%
mutate(FC_postRed_div_postYellow = Red/Yellow)
# bind back pval
red_v_yel_tobind_noOutlier <- post_noOutlier_t.test_paired %>%
dplyr::select(p)
# calculate log2FC, and -log10p
red_v_yel_volcano_data_noOutlier <-
bind_cols(red_v_yel_volcano_data_noOutlier, red_v_yel_tobind_noOutlier) %>%
mutate(log2_FC_postRed_div_postYellow = if_else(FC_postRed_div_postYellow > 0,
log2(FC_postRed_div_postYellow),
-(log2(abs(FC_postRed_div_postYellow)))),
neglog10p = -log10(p))
# create a df of features passing FC and pval cutoffs higher in post-Red
postRed_sig_red_v_yel_volcano_noOutlier <- red_v_yel_volcano_data_noOutlier %>%
filter(p <= 0.05 & log2_FC_postRed_div_postYellow >= 0.6)
# create a df of features passing FC and pval cutoffs higher in post-control
postYellow_sig_red_v_yel_volcano_noOutlier <- red_v_yel_volcano_data_noOutlier %>%
filter(p <= 0.05 & log2_FC_postRed_div_postYellow <= -0.6) Export sig features
Create feature lists with significant features along with their clusters
#post-Red list
postRed_sig_intrvntn_comp_clusters <- left_join(postRed_sig_red_v_yel_volcano_noOutlier, cluster_features,
by = "mz_rt")
write_csv(postRed_sig_intrvntn_comp_clusters,
"Feature lists/postRed-sigfeatures-intervntn-comp.csv")
#post-Yellow list
postYellow_sig_intrvntn_comp_clusters <- left_join(postYellow_sig_red_v_yel_volcano_noOutlier, cluster_features,
by = "mz_rt")
write_csv(postYellow_sig_intrvntn_comp_clusters,
"Feature lists/postYellow-sigfeatures-intervntn-comp.csv")Plot
(red_v_yellow_volcano_noOutlier <- red_v_yel_volcano_data_noOutlier %>%
ggplot(aes(x = log2_FC_postRed_div_postYellow, y = neglog10p,
text = glue("Mass_retention time: {mz_rt}
P-value: {p}
Fold change tomato/control: {round(FC_postRed_div_postYellow, 2)}"))) +
geom_point(color = "grey") +
geom_point(data = postRed_sig_intrvntn_comp_clusters,
aes(x = log2_FC_postRed_div_postYellow, y = neglog10p),
color = "tomato") +
geom_point(data = postYellow_sig_intrvntn_comp_clusters,
aes(x = log2_FC_postRed_div_postYellow, y = neglog10p),
color = "yellow2") +
geom_vline(xintercept = 0.6, linetype = "dashed", color = "grey") +
geom_vline(xintercept = -0.6, linetype = "dashed", color = "grey") +
geom_hline(yintercept = 1.3, linetype = "dashed", color = "grey") +
coord_cartesian(xlim = c(-5, 8)) +
labs(title = "Volcano Plot of Features Different in People After Tomato-Soy and Control Juice Consumption",
subtitle = "Red points are higher after tomato-soy juice consumption while yellow points are higher \nafter control tomato juice consumption. Subject 6106 removed",
caption = "Vertical dashed lines represent a log2 fold change > 0.6 or < -0.6, and horizontal dashed line represents an FDR corrected \np-value of 0.05.",
x = "Log2 Fold Change (TomatoSoy/Control)",
y = "-Log10(P-value)") +
theme_bw() +
theme(plot.title = element_text(size = 12, hjust = 0),
plot.caption = element_text(hjust = 0.5)))
(red_v_yellow_volcano_ggplotly_noOutlier <- ggplotly(red_v_yellow_volcano_noOutlier, tooltip = "text"))Save plots
# save volcano plot
ggsave(plot = red_v_yellow_volcano_noOutlier,
filename = "plots and figures/volcano plots/red_v_yellow_volcano_noOutlier.svg")
# save interactive volcano plot
saveWidget(widget = red_v_yellow_volcano_ggplotly_noOutlier,
file = "plots and figures/volcano plots/interactive_redvyellow_volcano_plot_noOutlier.html")Red
Wrangle (no outlier)
# calculate mean rel abund (not log) by sample, and avg fold change (FC) diff by feature
red_volcano_data_noOutlier <- df_for_stats %>%
filter(Intervention == "Red",
Subject != 6106) %>%
group_by(pre_post, mz_rt) %>%
summarize(mean_rel_abund = mean(rel_abund)) %>%
pivot_wider(names_from = pre_post, values_from = mean_rel_abund) %>%
mutate(FC_post_div_pre = post/pre)
# bind back pval
red_tobind_noOutlier <- red_noOutlier_t.test_paired %>%
dplyr::select(p)
# calculate log2FC, and -log10p
red_volcano_data_noOutlier <-
bind_cols(red_volcano_data_noOutlier, red_tobind_noOutlier) %>%
mutate(log2_FC_post_div_pre = log2(FC_post_div_pre),
neglog10p = -log10(p))
# create a df of features passing FC and pval cutoffs higher in post-intervention compared to pre
red_pre_v_post_volcano_noOutlier <- red_volcano_data_noOutlier %>%
filter(p <= 0.05 & abs(log2_FC_post_div_pre) >= 0.6)Export sig features
Create feature lists with significant features along with their clusters
Plot
(red_volcano_noOutlier <- red_volcano_data_noOutlier %>%
ggplot(aes(x = log2_FC_post_div_pre, y = neglog10p,
text = glue("Mass_retention time: {mz_rt}
P-value: {p}
Fold change post/pre: {round(FC_post_div_pre, 2)}"))) +
geom_point(color = "grey") +
geom_point(data = red_sig_prepost_comp_clusters,
aes(x = log2_FC_post_div_pre, y = neglog10p),
color = "tomato") +
geom_vline(xintercept = 0.6, linetype = "dashed", color = "grey") +
geom_vline(xintercept = -0.6, linetype = "dashed", color = "grey") +
geom_hline(yintercept = 1.3, linetype = "dashed", color = "grey") +
coord_cartesian(xlim = c(-5, 8)) +
labs(title = "Volcano Plot of Features Different in People After Tomato-Soy Juice Consumption",
subtitle = "Red points are higher after tomato-soy juice consumption when compared to prior to consumption",
caption = "Vertical dashed lines represent an abs(log fold change) > 0.6, and horizontal dashed line represents an FDR corrected \np-value of 0.05.",
x = "Log2 Fold Change (Post/Pre)",
y = "-Log10(P-value)") +
theme_bw() +
theme(plot.title = element_text(size = 12, hjust = 0),
plot.caption = element_text(hjust = 0.5)))
Save plots
Yellow
Wrangle (no outlier)
# calculate mean rel abund (not log) by sample, and avg fold change (FC) diff by feature
yel_volcano_data_noOutlier <- df_for_stats %>%
filter(Intervention == "Yellow",
Subject != 6106) %>%
group_by(pre_post, mz_rt) %>%
summarize(mean_rel_abund = mean(rel_abund)) %>%
pivot_wider(names_from = pre_post, values_from = mean_rel_abund) %>%
mutate(FC_post_div_pre = post/pre)
# bind back pval
yel_tobind_noOutlier <- ctrl_noOutlier_t.test_paired %>%
dplyr::select(p)
# calculate log2FC, and -log10p
yel_volcano_data_noOutlier <-
bind_cols(yel_volcano_data_noOutlier, yel_tobind_noOutlier) %>%
mutate(log2_FC_post_div_pre = log2(FC_post_div_pre),
neglog10p = -log10(p))
# create a df of features passing FC and pval cutoffs higher in post-intervention compared to pre
yel_pre_v_post_volcano_noOutlier <- yel_volcano_data_noOutlier %>%
filter(p <= 0.05 & abs(log2_FC_post_div_pre) >= 0.6)Export sig features
Create feature lists with significant features along with their clusters
Plot
(yel_volcano_noOutlier <- yel_volcano_data_noOutlier %>%
ggplot(aes(x = log2_FC_post_div_pre, y = neglog10p,
text = glue("Mass_retention time: {mz_rt}
P-value: {p}
Fold change post/pre: {round(FC_post_div_pre, 2)}"))) +
geom_point(color = "grey") +
geom_point(data = yel_sig_prepost_comp_clusters,
aes(x = log2_FC_post_div_pre, y = neglog10p),
color = "yellow2") +
geom_vline(xintercept = 0.6, linetype = "dashed", color = "grey") +
geom_vline(xintercept = -0.6, linetype = "dashed", color = "grey") +
geom_hline(yintercept = 1.3, linetype = "dashed", color = "grey") +
coord_cartesian(xlim = c(-5, 8)) +
labs(title = "Volcano Plot of Features Different in People After Control, Yellow Tomato Juice Consumption",
subtitle = "Yellow points are higher after control juice consumption when compared to prior to consumption.\nSubject 6106 removed",
caption = "Vertical dashed lines represent abs(log2 fold change) > 0.6, and horizontal dashed line represents an FDR corrected \np-value of 0.05.",
x = "Log2 Fold Change (Post/Pre)",
y = "-Log10(P-value)") +
theme_bw() +
theme(plot.title = element_text(size = 12, hjust = 0),
plot.caption = element_text(hjust = 0.5)))
Save plots
Joined lists
Here, I want to venn significant features before I begin to look more into them. I am interested in the following effects: tomato effect, lycopene and/or soy isoflavones effect.
Tomato effect
- Tomato effect: join a list that only keeps features that are both significant in pre vs. post-red and pre vs. post-yellow. Manually deleted the features that don’t have the same directionality
# keep only features present in both pre vs post red and pre vs post yellow
tomato_effect <- inner_join(red_sig_prepost_comp_clusters,
yel_sig_prepost_comp_clusters,
by = "mz_rt")
dim(tomato_effect)## [1] 6 25Export venned list
Mummichog matches
# read in hits from red
red_mummichog_hits <- read_csv("../mummichog analysis/red-c18-mixed mode results-integ/red-c18-mummichog_matched_compound_all.csv") %>%
mutate(Query.Mass = round(Query.Mass, digits = 4)) %>% # keep 4 decimal points for mass
unite(mz_rt,
c("Query.Mass", "Retention.Time"),
sep = "_")Mummichog makes a list of hits for all of the compounds, so we only need to do an inner join for one of the lists. The outcome would be the same for all of the lists used since I have to input my whole dataset (no cutoffs) into analysis.
mummichog_mass_matches_tomato <- inner_join(tomato_effect,
red_mummichog_hits,
by = "mz_rt")
length(unique(mummichog_mass_matches_tomato$mz_rt)) # how many?## [1] 0## # A tibble: 0 × 1
## # ℹ 1 variable: unique(mummichog_mass_matches_tomato$mz_rt) <chr>Boxplots
all sig tomato features
tomato_effect_df_for_stats_wide <- df_for_stats %>%
dplyr::select(!rel_abund) %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2) %>%
dplyr::select(c(1:11),
(all_of(sigmetabs_tomato_effect)))
# make tidy df
tomato_effect_df_for_stats_tidy <- tomato_effect_df_for_stats_wide %>%
pivot_longer(cols = 12:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund_log2")# changing factor levels for pre_post_intervention
tomato_effect_df_for_stats_tidy$pre_post_intervention <- factor(tomato_effect_df_for_stats_tidy$pre_post_intervention,
levels = c("pre_Yellow", "post_Yellow", "pre_Red", "post_Red"))
levels(tomato_effect_df_for_stats_tidy$pre_post_intervention) ## [1] "pre_Yellow" "post_Yellow" "pre_Red" "post_Red"tomato_effect_df_for_stats_tidy %>%
ggplot(aes(x = pre_post_intervention, y = rel_abund_log2, fill = Intervention)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = c("Yellow" = "gold",
"Red" = "tomato1")) +
geom_line(aes(group = Subject), fill = "brown", linewidth = 0.2) +
facet_wrap(vars(mz_rt), scales = "free_y") +
theme_clean() 
Lyc/soy effect
- lycopene and/or soy isoflavones effect: join a list that only keeps features that are:
- significantly different between post-Red and post-Yellow,
- and significant between pre- and post-Red.
- remove features that are significant between pre-Yellow and post-Yellow.
# combine sig features from post-red vs post-yellow
sig_postintervention_noOutlier <- full_join(postRed_sig_intrvntn_comp_clusters,
postYellow_sig_intrvntn_comp_clusters)
dim(sig_postintervention_noOutlier)## [1] 59 13# select only sig features that are present when comparing pre-Red to post-Red
lyc_soy_effect <- inner_join(sig_postintervention_noOutlier,
red_sig_prepost_comp_clusters,
by = "mz_rt")
dim(lyc_soy_effect)## [1] 53 25Export venned list
Mummichog matches
Mummichog makes a list of hits for all of the compounds, so we only need to do an inner join for one of the lists. The outcome would be the same for all of the lists used since I have to input my whole dataset (no cutoffs) into analysis.
mummichog_mass_matches_lycsoy <- inner_join(lyc_soy_effect,
red_mummichog_hits,
by = "mz_rt")
length(unique(mummichog_mass_matches_lycsoy$mz_rt)) # how many?## [1] 3## # A tibble: 3 × 1
## `unique(mummichog_mass_matches_lycsoy$mz_rt)`
## <chr>
## 1 253.0503_5.048
## 2 359.1353_0.806
## 3 361.1499_3.066Boxplots
all sig tomato features
lycsoy_effect_df_for_stats_wide <- df_for_stats %>%
dplyr::select(!rel_abund) %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2) %>%
dplyr::select(c(1:11),
all_of(sigmetabs_lycsoy_effect))
# make tidy df
lycsoy_effect_df_for_stats_tidy <- lycsoy_effect_df_for_stats_wide %>%
pivot_longer(cols = 12:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund_log2")# changing factor levels for pre_post_intervention
lycsoy_effect_df_for_stats_tidy$pre_post_intervention <- factor(lycsoy_effect_df_for_stats_tidy$pre_post_intervention,
levels = c("pre_Yellow", "post_Yellow", "pre_Red", "post_Red"))
levels(lycsoy_effect_df_for_stats_tidy$pre_post_intervention) ## [1] "pre_Yellow" "post_Yellow" "pre_Red" "post_Red"lycsoy_effect_df_for_stats_tidy %>%
ggplot(aes(x = pre_post_intervention, y = rel_abund_log2, fill = Intervention)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = c("Yellow" = "gold",
"Red" = "tomato1")) +
geom_line(aes(group = Subject), linewidth = 0.2) +
facet_wrap(vars(mz_rt), scales = "free_y") +
theme_clean() 
Low carotenoid tomato effect
- yellow tomato effect: use list that only keeps features that are both significant between post-Red and post-Yellow timepoints, and significant between pre- and post-Yellow. These features are NOT significant between pre-Yellow and post-Yellow.
## [1] 59 13# select only sig features that are present when comparing pre-Yellow to post-Yellow
low_carot_tomato_effect <- inner_join(sig_postintervention_noOutlier,
yel_sig_prepost_comp_clusters,
by = "mz_rt")
dim(low_carot_tomato_effect)## [1] 3 25Export venned list
Mummichog masses
mummichog_mass_matches_yellow_tomato <- inner_join(low_carot_tomato_effect,
red_mummichog_hits,
by = "mz_rt")
tibble(unique(mummichog_mass_matches_yellow_tomato$mz_rt)) # which features have a hit with mummichog?## # A tibble: 0 × 1
## # ℹ 1 variable: unique(mummichog_mass_matches_yellow_tomato$mz_rt) <chr>## [1] 0Boxplots
all sig yellow tom features
# metabs with pval < 0.05 and fc >= 1.51
sigmetabs_yellow_effect <- as.character(low_carot_tomato_effect$mz_rt)yellow_effect_df_for_stats_wide <- df_for_stats %>%
dplyr::select(!rel_abund) %>%
pivot_wider(names_from = mz_rt,
values_from = rel_abund_log2) %>%
dplyr::select(c(1:11),
all_of(sigmetabs_yellow_effect))
# make tidy df
yellow_effect_df_for_stats_tidy <- yellow_effect_df_for_stats_wide %>%
pivot_longer(cols = 12:ncol(.),
names_to = "mz_rt",
values_to = "rel_abund_log2")# changing factor levels for pre_post_intervention
yellow_effect_df_for_stats_tidy$pre_post_intervention <- factor(yellow_effect_df_for_stats_tidy$pre_post_intervention,
levels = c("pre_Yellow", "post_Yellow", "pre_Red", "post_Red"))
levels(yellow_effect_df_for_stats_tidy$pre_post_intervention) ## [1] "pre_Yellow" "post_Yellow" "pre_Red" "post_Red"yellow_effect_df_for_stats_tidy %>%
ggplot(aes(x = pre_post_intervention, y = rel_abund_log2, fill = Intervention)) +
geom_boxplot(outlier.shape = NA) +
scale_fill_manual(values = c("Yellow" = "gold",
"Red" = "tomato1")) +
geom_line(aes(group = Subject), fill = "brown", linewidth = 0.2) +
facet_wrap(vars(mz_rt), scales = "free_y") +
theme_clean() 
Immuno Correlations
IL-5, IL-12p70, and GM-CSF were significantly decreased after tomato-soy intervention (only when comparing pre to post in Red) according to Wilcoxon rank-sum tests (p<0.05). Immune cell types were significantly altered in all 3 comparisons - so let’s see how these significant immuno outcomes correlate with metabolites found to be significant at p > 0.05 and FC >=1.61.
Import other outcomes (carotenoids and immunology data)
# load data
carot_immunology_meta <- read_excel("CompiledData_Results_Meta.xlsx",
sheet = "metadata_corrected_withsequence")
# clean up variable names
carot_immunology_meta <- clean_names(carot_immunology_meta)Wrangle
# convert variables that should be factors to factors
carot_immunology_meta <- carot_immunology_meta %>%
filter(intervention != "Baseline") %>%
mutate(across(.cols = c("patient_id", "period",
"intervention", "intervention_week",
"pre_post", "sex", "sequence"),
.fns = as.factor))
# some stuff came in as characters but should be numeric
carot_immunology_meta <- carot_immunology_meta %>%
mutate(across(.cols = c("il_2", "il_10", "il_13", "il_4"),
.fns = as.numeric))
# changing factor levels for pre_post
carot_immunology_meta$pre_post <- factor(carot_immunology_meta$pre_post,
levels = c("pre", "post"))
levels(carot_immunology_meta$pre_post) ## [1] "pre" "post"# Calculate total_cis_lyc, total_lyc, and total_carotenoids
carot_immunology_meta <- carot_immunology_meta %>%
mutate(total_cis_lyc = other_cis_lyc + x5_cis_lyc,
total_lyc = all_trans_lyc + total_cis_lyc,
total_carotenoids = lutein + zeaxanthin + b_cryptoxanthin +
a_carotene + b_carotene + total_lyc)Rm outliers
# take outliers out of carot_immuno df
carot_immunology_meta_noOutlier <- carot_immunology_meta %>%
filter(patient_id != 6106)
# add sig immuno outcome columns to dataset
forcorr_metabslog2_immuno_noOutlier <- df_for_stats_wide_noOutlier %>%
mutate(il_5 = carot_immunology_meta_noOutlier$il_5,
il_12p70 = carot_immunology_meta_noOutlier$il_12p70,
gm_csf = carot_immunology_meta_noOutlier$gm_csf,
total_lyc = carot_immunology_meta_noOutlier$total_lyc,
# sig immune cells pre vs. post red
CD45ROpos_CD45RAneg_EMCD8 = carot_immunology_meta_noOutlier$x08_cd45ro_cd45ra_em_cd8,
CD19posCD3neg_Bcells = carot_immunology_meta_noOutlier$x25_cd19_cd3_b_cells,
CD27neg_IgDpos_naiveBcells = carot_immunology_meta_noOutlier$x26_cd27_ig_d_naive_b_cells,
# sig immune cells post-yellow vs. post red
CD16negNKcells = carot_immunology_meta_noOutlier$x38_cd16_nk_cells,
CD14posMDSCs = carot_immunology_meta_noOutlier$x40_cd14_mdsc_mono)Here, I am going to look at correlations between fold change differences and sig metabolites from the lycopene/soy effect list
More wrangling
No outliers
Subset df’s into pre and post
# pre
forcorr_pre_metabslog2_immuno_noOutlier <- forcorr_metabslog2_immuno_noOutlier %>%
dplyr::select(Subject, Intervention, pre_post, 12:ncol(.)) %>%
filter(pre_post == "pre")
# post
forcorr_post_metabslog2_immuno_noOutlier <- forcorr_metabslog2_immuno_noOutlier %>%
dplyr::select(Subject, Intervention, pre_post, 12:ncol(.)) %>%
filter(pre_post == "post")Create df with differences calculated for each outcome
Lycsoy metab list Corr
These are correlations based on significant metabolites with FDR < 0.05 AND FC > 1.51 (from lyc/soy effect list) against immuno outcomes that were significant (padj < 0.05) when comparing pre to post red intervention.
# add subject and intervention back to df. Select only significant metabolites (pval > 0.05 and FC >=1.51) that are in lyc/soy effect list
lyc_soy_forcorr_diff_red_metabslog2_immuno_noOutlier <- forcorr_diff_red_metabslog2_immuno_noOutlier %>%
mutate(Subject = forcorr_post_metabslog2_immuno_noOutlier$Subject,
Intervention = forcorr_post_metabslog2_immuno_noOutlier$Intervention) %>%
filter(Intervention == "Red") %>%
dplyr::select(Subject, Intervention, il_5, il_12p70, gm_csf, total_lyc, CD45ROpos_CD45RAneg_EMCD8, CD19posCD3neg_Bcells, CD27neg_IgDpos_naiveBcells, CD16negNKcells, CD14posMDSCs, all_of(lyc_soy_effect$mz_rt))
# correlation table
lycsoy_red_corr_results_metabs_immuno_diff_noOutlier <- lyc_soy_forcorr_diff_red_metabslog2_immuno_noOutlier %>%
correlate(method = "spearman") %>%
rearrange() %>%
shave()
kable(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier, format = "markdown", digits = 3)| term | il_12p70 | CD14posMDSCs | il_5 | CD16negNKcells | CD45ROpos_CD45RAneg_EMCD8 | gm_csf | total_lyc | CD19posCD3neg_Bcells | CD27neg_IgDpos_naiveBcells | 605.1126_3.023 | 447.0928_3.777 | 255.0674_5.078 | 217.0173_3.88 | 525.0334_3.606 | 393.0495_3.605 | 497.0695_3.785 | 217.0172_4.4 | 576.1384_0.705 | 445.0782_4.484 | 257.0816_6.056 | 447.092_4.187 | 459.093_3.801 | 369.0829_3.476 | 335.0244_4.36 | 431.0979_3.907 | 1122.0249_4.032 | 521.1082_4.508 | 365.0863_4.095 | 201.0227_4.086 | 509.0409_3.291 | 1122.0248_4.125 | 617.1836_4.515 | 433.1136_4.519 | 253.0503_5.048 | 429.083_3.201 | 297.0975_4.072 | 509.0384_0.699 | 413.1436_3.2 | 449.1074_4.301 | 427.197_4.049 | 359.1353_0.806 | 201.0228_4.555 | 493.1666_4.573 | 366.0851_4.55 | 363.021_3.933 | 433.113_4.886 | 363.0234_4.453 | 297.0975_4.498 | 427.197_3.64 | 691.3179_4.171 | 413.1426_4.219 | 639.0783_3.758 | 345.1933_4.201 | 373.1141_3.685 | 345.1554_4.506 | 333.0047_4.33 | 572.2105_4.512 | 429.082_3.756 | 413.1426_4.51 | 361.1499_3.066 | 429.0815_4.032 | 333.0066_3.893 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| il_12p70 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| CD14posMDSCs | 0.200 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| il_5 | 0.573 | 0.300 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| CD16negNKcells | 0.500 | 0.336 | 0.655 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| CD45ROpos_CD45RAneg_EMCD8 | 0.527 | -0.173 | 0.127 | 0.082 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| gm_csf | 0.382 | 0.091 | -0.064 | -0.300 | 0.618 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| total_lyc | -0.009 | 0.264 | -0.009 | 0.355 | -0.045 | 0.045 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| CD19posCD3neg_Bcells | 0.255 | -0.364 | 0.073 | 0.336 | 0.500 | 0.200 | 0.236 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| CD27neg_IgDpos_naiveBcells | 0.109 | -0.464 | -0.036 | 0.200 | 0.473 | 0.200 | 0.300 | 0.973 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 605.1126_3.023 | -0.609 | 0.282 | -0.136 | -0.200 | -0.073 | -0.045 | -0.155 | -0.082 | -0.055 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 447.0928_3.777 | -0.073 | 0.164 | -0.027 | -0.191 | 0.282 | 0.745 | 0.264 | 0.145 | 0.200 | 0.291 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 255.0674_5.078 | -0.273 | -0.218 | -0.073 | -0.682 | 0.291 | 0.436 | -0.418 | -0.245 | -0.155 | 0.382 | 0.464 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 217.0173_3.88 | -0.182 | 0.109 | -0.327 | -0.118 | -0.027 | 0.200 | 0.191 | -0.345 | -0.273 | 0.100 | 0.282 | -0.045 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 525.0334_3.606 | -0.382 | 0.191 | -0.309 | -0.155 | 0.200 | 0.164 | -0.173 | 0.109 | 0.109 | 0.709 | 0.436 | 0.327 | -0.009 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 393.0495_3.605 | -0.236 | 0.318 | -0.164 | -0.091 | -0.218 | 0.200 | 0.145 | -0.500 | -0.445 | 0.145 | 0.545 | 0.118 | 0.727 | 0.291 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 497.0695_3.785 | -0.155 | 0.173 | -0.182 | -0.109 | 0.164 | 0.482 | -0.109 | 0.127 | 0.118 | 0.427 | 0.764 | 0.273 | 0.255 | 0.773 | 0.609 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 217.0172_4.4 | -0.127 | 0.109 | -0.382 | -0.273 | 0.173 | 0.509 | 0.082 | -0.245 | -0.173 | 0.109 | 0.645 | 0.245 | 0.764 | 0.400 | 0.873 | 0.700 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 576.1384_0.705 | -0.255 | -0.691 | -0.100 | -0.209 | 0.318 | -0.291 | -0.491 | 0.118 | 0.191 | 0.173 | -0.327 | 0.373 | -0.055 | 0.118 | -0.291 | -0.182 | -0.127 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 445.0782_4.484 | -0.509 | 0.245 | -0.209 | -0.018 | -0.282 | 0.018 | 0.109 | 0.073 | 0.082 | 0.564 | 0.555 | 0.091 | 0.000 | 0.764 | 0.473 | 0.791 | 0.355 | -0.273 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 257.0816_6.056 | -0.427 | -0.573 | -0.227 | -0.664 | 0.100 | -0.064 | -0.727 | -0.191 | -0.100 | 0.327 | -0.091 | 0.745 | -0.109 | 0.227 | -0.136 | 0.018 | -0.009 | 0.791 | -0.082 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 447.092_4.187 | -0.073 | -0.091 | -0.318 | -0.127 | 0.055 | 0.473 | -0.064 | 0.336 | 0.309 | 0.091 | 0.627 | 0.045 | 0.127 | 0.482 | 0.382 | 0.836 | 0.509 | -0.300 | 0.673 | -0.109 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 459.093_3.801 | -0.309 | 0.091 | -0.227 | -0.073 | -0.064 | 0.200 | -0.155 | 0.200 | 0.155 | 0.455 | 0.536 | 0.173 | -0.109 | 0.773 | 0.318 | 0.845 | 0.318 | -0.191 | 0.900 | 0.027 | 0.836 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 369.0829_3.476 | -0.273 | -0.018 | -0.073 | 0.236 | 0.082 | -0.364 | -0.082 | -0.173 | -0.164 | 0.255 | -0.036 | 0.036 | 0.218 | 0.527 | 0.418 | 0.300 | 0.345 | 0.427 | 0.345 | 0.173 | 0.000 | 0.273 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 335.0244_4.36 | -0.300 | -0.655 | -0.209 | -0.518 | 0.255 | 0.209 | -0.145 | -0.100 | 0.036 | 0.009 | 0.209 | 0.655 | 0.282 | -0.155 | 0.064 | -0.100 | 0.209 | 0.573 | -0.264 | 0.618 | -0.091 | -0.191 | 0.073 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 431.0979_3.907 | -0.364 | -0.300 | -0.109 | -0.536 | -0.091 | 0.345 | -0.245 | -0.300 | -0.218 | 0.191 | 0.536 | 0.691 | 0.309 | 0.018 | 0.409 | 0.318 | 0.382 | 0.109 | 0.191 | 0.464 | 0.327 | 0.264 | -0.055 | 0.736 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 1122.0249_4.032 | 0.027 | -0.827 | -0.109 | -0.191 | 0.145 | 0.191 | -0.064 | 0.355 | 0.418 | -0.455 | 0.127 | 0.136 | 0.073 | -0.409 | -0.109 | -0.064 | 0.036 | 0.291 | -0.227 | 0.236 | 0.300 | -0.045 | -0.218 | 0.655 | 0.527 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 521.1082_4.508 | -0.382 | 0.009 | -0.127 | -0.118 | -0.309 | 0.182 | 0.345 | -0.200 | -0.109 | 0.055 | 0.682 | 0.191 | 0.409 | 0.164 | 0.773 | 0.536 | 0.609 | -0.318 | 0.600 | -0.136 | 0.518 | 0.455 | 0.218 | 0.264 | 0.627 | 0.282 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 365.0863_4.095 | -0.327 | -0.109 | -0.245 | 0.064 | -0.218 | 0.091 | 0.536 | 0.109 | 0.209 | -0.064 | 0.564 | -0.136 | 0.455 | 0.155 | 0.673 | 0.500 | 0.600 | -0.264 | 0.564 | -0.309 | 0.555 | 0.391 | 0.264 | 0.118 | 0.309 | 0.327 | 0.882 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 201.0227_4.086 | 0.136 | -0.727 | -0.218 | -0.155 | 0.373 | 0.391 | -0.109 | 0.400 | 0.427 | -0.409 | 0.273 | 0.145 | 0.191 | -0.145 | 0.045 | 0.209 | 0.291 | 0.255 | -0.100 | 0.173 | 0.518 | 0.164 | -0.027 | 0.582 | 0.482 | 0.909 | 0.300 | 0.373 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 509.0409_3.291 | -0.536 | -0.536 | -0.609 | -0.355 | 0.055 | 0.045 | 0.155 | 0.400 | 0.491 | 0.273 | 0.109 | 0.100 | 0.145 | 0.109 | -0.227 | 0.009 | -0.027 | 0.282 | 0.118 | 0.218 | 0.245 | 0.182 | -0.109 | 0.491 | 0.300 | 0.482 | 0.091 | 0.209 | 0.455 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 1122.0248_4.125 | 0.091 | -0.855 | -0.218 | -0.164 | 0.273 | 0.182 | -0.264 | 0.418 | 0.455 | -0.427 | 0.045 | 0.064 | 0.118 | -0.200 | -0.073 | 0.100 | 0.164 | 0.427 | -0.182 | 0.309 | 0.418 | 0.064 | -0.027 | 0.518 | 0.355 | 0.909 | 0.136 | 0.273 | 0.936 | 0.409 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 617.1836_4.515 | -0.255 | -0.209 | -0.282 | 0.118 | -0.064 | 0.127 | 0.527 | 0.209 | 0.300 | -0.082 | 0.491 | -0.209 | 0.618 | 0.064 | 0.600 | 0.418 | 0.609 | -0.136 | 0.382 | -0.309 | 0.509 | 0.264 | 0.255 | 0.236 | 0.300 | 0.445 | 0.755 | 0.936 | 0.518 | 0.373 | 0.418 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 433.1136_4.519 | -0.409 | -0.573 | 0.027 | -0.100 | -0.309 | -0.464 | -0.564 | 0.018 | 0.027 | 0.218 | -0.273 | 0.136 | -0.055 | -0.055 | -0.145 | -0.045 | -0.255 | 0.582 | 0.055 | 0.582 | 0.064 | 0.145 | 0.145 | 0.373 | 0.418 | 0.464 | 0.027 | -0.036 | 0.309 | 0.336 | 0.482 | 0.036 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 253.0503_5.048 | -0.336 | -0.536 | -0.436 | -0.791 | 0.218 | 0.409 | -0.500 | -0.164 | -0.073 | 0.145 | 0.364 | 0.827 | 0.091 | 0.273 | 0.164 | 0.327 | 0.373 | 0.418 | 0.100 | 0.755 | 0.336 | 0.300 | 0.073 | 0.745 | 0.764 | 0.473 | 0.282 | 0.055 | 0.545 | 0.364 | 0.482 | 0.045 | 0.309 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 429.083_3.201 | -0.200 | -0.291 | -0.236 | -0.218 | 0.000 | 0.327 | -0.327 | 0.182 | 0.145 | 0.209 | 0.445 | 0.236 | 0.173 | 0.282 | 0.227 | 0.591 | 0.300 | -0.009 | 0.445 | 0.191 | 0.791 | 0.691 | -0.018 | 0.291 | 0.655 | 0.518 | 0.427 | 0.318 | 0.645 | 0.473 | 0.545 | 0.382 | 0.500 | 0.545 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 297.0975_4.072 | -0.064 | -0.127 | -0.109 | 0.100 | 0.118 | 0.245 | 0.118 | -0.009 | 0.018 | -0.091 | 0.564 | 0.091 | 0.473 | 0.318 | 0.755 | 0.664 | 0.755 | -0.036 | 0.473 | -0.091 | 0.609 | 0.491 | 0.564 | 0.236 | 0.418 | 0.327 | 0.773 | 0.782 | 0.545 | -0.009 | 0.409 | 0.764 | 0.036 | 0.309 | 0.482 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 509.0384_0.699 | -0.427 | -0.318 | -0.427 | -0.336 | 0.282 | 0.009 | -0.455 | -0.027 | -0.018 | 0.500 | 0.064 | 0.482 | 0.082 | 0.609 | 0.036 | 0.318 | 0.200 | 0.591 | 0.255 | 0.600 | 0.200 | 0.436 | 0.564 | 0.455 | 0.336 | 0.073 | -0.009 | -0.100 | 0.273 | 0.473 | 0.218 | -0.009 | 0.382 | 0.636 | 0.473 | 0.255 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 413.1436_3.2 | -0.636 | -0.391 | -0.327 | -0.191 | -0.436 | -0.455 | -0.009 | 0.055 | 0.182 | 0.145 | 0.000 | 0.000 | -0.064 | 0.309 | 0.218 | 0.245 | 0.127 | 0.282 | 0.509 | 0.300 | 0.227 | 0.318 | 0.364 | 0.009 | 0.036 | 0.118 | 0.382 | 0.518 | 0.018 | 0.127 | 0.200 | 0.355 | 0.364 | 0.127 | 0.027 | 0.282 | 0.064 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 449.1074_4.301 | -0.582 | -0.055 | -0.100 | 0.036 | -0.364 | -0.182 | 0.482 | -0.109 | 0.000 | 0.200 | 0.445 | 0.100 | 0.236 | 0.200 | 0.545 | 0.300 | 0.318 | -0.100 | 0.600 | -0.100 | 0.236 | 0.364 | 0.418 | 0.273 | 0.436 | 0.173 | 0.864 | 0.809 | 0.118 | 0.236 | -0.009 | 0.691 | 0.127 | 0.118 | 0.218 | 0.609 | 0.109 | 0.509 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 427.197_4.049 | -0.418 | -0.118 | -0.573 | 0.118 | -0.282 | -0.345 | 0.309 | 0.000 | 0.045 | -0.027 | -0.045 | -0.445 | 0.418 | 0.282 | 0.455 | 0.264 | 0.418 | -0.009 | 0.427 | -0.236 | 0.336 | 0.318 | 0.591 | -0.100 | -0.136 | 0.036 | 0.409 | 0.655 | 0.182 | 0.282 | 0.182 | 0.673 | 0.036 | -0.082 | 0.155 | 0.564 | 0.245 | 0.518 | 0.509 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 359.1353_0.806 | -0.082 | -0.473 | -0.191 | 0.136 | 0.209 | 0.027 | -0.118 | 0.273 | 0.300 | -0.045 | 0.191 | -0.100 | 0.518 | 0.200 | 0.409 | 0.455 | 0.536 | 0.391 | 0.191 | 0.127 | 0.482 | 0.273 | 0.500 | 0.291 | 0.227 | 0.500 | 0.345 | 0.545 | 0.664 | 0.255 | 0.709 | 0.700 | 0.418 | 0.227 | 0.500 | 0.736 | 0.345 | 0.345 | 0.264 | 0.564 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 201.0228_4.555 | -0.155 | -0.618 | -0.309 | -0.109 | 0.164 | 0.173 | 0.145 | 0.245 | 0.300 | -0.218 | 0.218 | 0.073 | 0.355 | -0.182 | 0.109 | 0.055 | 0.236 | 0.218 | -0.045 | 0.073 | 0.336 | 0.100 | 0.082 | 0.682 | 0.545 | 0.818 | 0.418 | 0.464 | 0.855 | 0.682 | 0.727 | 0.645 | 0.345 | 0.445 | 0.609 | 0.509 | 0.373 | -0.018 | 0.400 | 0.355 | 0.582 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 493.1666_4.573 | -0.273 | -0.309 | -0.355 | -0.182 | 0.145 | 0.218 | -0.064 | -0.118 | -0.073 | 0.127 | 0.345 | 0.227 | 0.709 | 0.145 | 0.527 | 0.364 | 0.618 | 0.245 | 0.136 | 0.191 | 0.373 | 0.236 | 0.391 | 0.627 | 0.636 | 0.473 | 0.500 | 0.455 | 0.636 | 0.473 | 0.500 | 0.636 | 0.327 | 0.518 | 0.645 | 0.682 | 0.582 | -0.036 | 0.400 | 0.436 | 0.709 | 0.800 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 366.0851_4.55 | -0.209 | -0.155 | -0.227 | -0.155 | -0.082 | 0.245 | -0.145 | -0.118 | -0.118 | 0.009 | 0.536 | 0.255 | 0.245 | 0.382 | 0.636 | 0.700 | 0.600 | -0.109 | 0.600 | 0.082 | 0.755 | 0.718 | 0.391 | 0.236 | 0.609 | 0.355 | 0.764 | 0.627 | 0.545 | 0.091 | 0.400 | 0.545 | 0.209 | 0.536 | 0.736 | 0.864 | 0.400 | 0.273 | 0.555 | 0.445 | 0.536 | 0.500 | 0.636 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 363.021_3.933 | -0.373 | -0.318 | -0.245 | -0.155 | -0.064 | -0.136 | 0.109 | -0.255 | -0.182 | -0.091 | 0.145 | 0.300 | 0.091 | 0.136 | 0.336 | 0.082 | 0.255 | 0.218 | 0.227 | 0.200 | 0.082 | 0.218 | 0.582 | 0.527 | 0.445 | 0.300 | 0.564 | 0.436 | 0.355 | 0.209 | 0.209 | 0.373 | 0.127 | 0.473 | 0.209 | 0.591 | 0.491 | 0.264 | 0.691 | 0.464 | 0.245 | 0.555 | 0.527 | 0.627 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 433.113_4.886 | -0.609 | -0.482 | -0.145 | -0.100 | -0.455 | -0.555 | -0.382 | -0.073 | -0.036 | 0.318 | -0.236 | 0.036 | 0.136 | 0.027 | 0.036 | 0.009 | -0.118 | 0.500 | 0.200 | 0.473 | 0.091 | 0.200 | 0.273 | 0.345 | 0.409 | 0.373 | 0.182 | 0.155 | 0.236 | 0.436 | 0.382 | 0.227 | 0.945 | 0.245 | 0.491 | 0.136 | 0.418 | 0.464 | 0.327 | 0.291 | 0.473 | 0.400 | 0.445 | 0.273 | 0.236 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 363.0234_4.453 | -0.345 | -0.427 | -0.227 | -0.118 | 0.009 | -0.036 | 0.300 | -0.055 | 0.045 | -0.155 | 0.255 | 0.255 | 0.145 | 0.009 | 0.282 | 0.045 | 0.245 | 0.191 | 0.173 | 0.109 | 0.118 | 0.145 | 0.427 | 0.627 | 0.491 | 0.491 | 0.627 | 0.564 | 0.500 | 0.373 | 0.336 | 0.545 | 0.118 | 0.436 | 0.245 | 0.600 | 0.373 | 0.245 | 0.755 | 0.436 | 0.309 | 0.718 | 0.573 | 0.573 | 0.945 | 0.227 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 297.0975_4.498 | -0.336 | -0.336 | -0.445 | -0.018 | -0.182 | 0.100 | 0.355 | 0.291 | 0.345 | -0.073 | 0.391 | -0.218 | 0.409 | 0.073 | 0.382 | 0.400 | 0.409 | -0.173 | 0.445 | -0.236 | 0.682 | 0.455 | 0.082 | 0.218 | 0.373 | 0.573 | 0.673 | 0.818 | 0.636 | 0.600 | 0.536 | 0.882 | 0.218 | 0.182 | 0.655 | 0.636 | 0.136 | 0.327 | 0.600 | 0.664 | 0.618 | 0.764 | 0.636 | 0.636 | 0.382 | 0.382 | 0.536 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 427.197_3.64 | -0.536 | -0.236 | -0.627 | 0.027 | -0.300 | -0.391 | 0.309 | 0.027 | 0.100 | 0.018 | -0.064 | -0.364 | 0.364 | 0.273 | 0.373 | 0.200 | 0.345 | 0.091 | 0.418 | -0.127 | 0.291 | 0.300 | 0.573 | 0.018 | -0.073 | 0.109 | 0.409 | 0.645 | 0.209 | 0.409 | 0.218 | 0.664 | 0.118 | 0.000 | 0.164 | 0.509 | 0.309 | 0.582 | 0.564 | 0.982 | 0.536 | 0.418 | 0.445 | 0.418 | 0.527 | 0.373 | 0.518 | 0.682 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 691.3179_4.171 | -0.555 | -0.291 | -0.300 | -0.136 | -0.645 | -0.445 | -0.064 | -0.145 | -0.091 | -0.036 | 0.000 | -0.073 | -0.091 | 0.182 | 0.327 | 0.264 | 0.109 | -0.009 | 0.582 | 0.127 | 0.409 | 0.500 | 0.327 | -0.018 | 0.245 | 0.227 | 0.582 | 0.573 | 0.164 | 0.091 | 0.236 | 0.382 | 0.418 | 0.200 | 0.327 | 0.445 | 0.109 | 0.800 | 0.618 | 0.591 | 0.291 | 0.191 | 0.136 | 0.618 | 0.518 | 0.527 | 0.445 | 0.518 | 0.627 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 413.1426_4.219 | -0.573 | -0.718 | -0.364 | -0.118 | -0.245 | -0.591 | -0.082 | 0.127 | 0.245 | -0.018 | -0.291 | -0.045 | 0.000 | 0.082 | -0.009 | -0.073 | -0.027 | 0.636 | 0.145 | 0.427 | 0.009 | 0.064 | 0.491 | 0.336 | 0.064 | 0.400 | 0.182 | 0.364 | 0.300 | 0.382 | 0.482 | 0.364 | 0.582 | 0.218 | 0.073 | 0.264 | 0.327 | 0.809 | 0.427 | 0.582 | 0.527 | 0.364 | 0.245 | 0.209 | 0.473 | 0.664 | 0.482 | 0.382 | 0.682 | 0.664 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 639.0783_3.758 | -0.436 | -0.327 | -0.509 | -0.355 | -0.036 | 0.091 | -0.300 | 0.045 | 0.055 | 0.218 | 0.309 | 0.327 | -0.064 | 0.609 | 0.255 | 0.600 | 0.327 | 0.145 | 0.636 | 0.336 | 0.691 | 0.809 | 0.409 | 0.218 | 0.427 | 0.255 | 0.436 | 0.355 | 0.445 | 0.373 | 0.364 | 0.273 | 0.300 | 0.645 | 0.700 | 0.555 | 0.700 | 0.391 | 0.391 | 0.464 | 0.382 | 0.418 | 0.473 | 0.818 | 0.609 | 0.355 | 0.509 | 0.518 | 0.500 | 0.636 | 0.391 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 345.1933_4.201 | -0.718 | -0.300 | -0.645 | -0.318 | -0.364 | -0.118 | 0.245 | 0.109 | 0.218 | 0.200 | 0.318 | 0.045 | 0.073 | 0.427 | 0.318 | 0.427 | 0.300 | -0.045 | 0.709 | 0.064 | 0.564 | 0.618 | 0.264 | 0.136 | 0.282 | 0.236 | 0.636 | 0.709 | 0.264 | 0.536 | 0.218 | 0.591 | 0.164 | 0.327 | 0.418 | 0.464 | 0.309 | 0.700 | 0.709 | 0.691 | 0.300 | 0.400 | 0.318 | 0.591 | 0.545 | 0.355 | 0.573 | 0.736 | 0.764 | 0.782 | 0.582 | 0.736 | NA | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 373.1141_3.685 | -0.636 | -0.473 | -0.227 | 0.009 | -0.509 | -0.636 | -0.045 | -0.018 | 0.073 | 0.100 | -0.145 | -0.127 | 0.109 | 0.127 | 0.255 | 0.118 | 0.073 | 0.382 | 0.400 | 0.264 | 0.164 | 0.255 | 0.509 | 0.173 | 0.182 | 0.300 | 0.436 | 0.545 | 0.191 | 0.245 | 0.345 | 0.491 | 0.645 | 0.100 | 0.218 | 0.409 | 0.209 | 0.855 | 0.618 | 0.636 | 0.555 | 0.300 | 0.291 | 0.391 | 0.445 | 0.773 | 0.436 | 0.500 | 0.700 | 0.827 | 0.900 | 0.418 | 0.645 | NA | NA | NA | NA | NA | NA | NA | NA | NA |
| 345.1554_4.506 | -0.618 | -0.709 | -0.345 | -0.173 | -0.318 | -0.618 | -0.136 | 0.055 | 0.173 | 0.009 | -0.300 | 0.018 | -0.055 | 0.073 | -0.018 | -0.091 | -0.073 | 0.627 | 0.164 | 0.482 | -0.009 | 0.082 | 0.464 | 0.355 | 0.127 | 0.391 | 0.200 | 0.327 | 0.264 | 0.364 | 0.445 | 0.300 | 0.627 | 0.264 | 0.091 | 0.227 | 0.336 | 0.818 | 0.445 | 0.527 | 0.455 | 0.336 | 0.218 | 0.227 | 0.500 | 0.700 | 0.491 | 0.345 | 0.636 | 0.709 | 0.991 | 0.418 | 0.591 | 0.909 | NA | NA | NA | NA | NA | NA | NA | NA |
| 333.0047_4.33 | -0.336 | -0.591 | -0.473 | -0.409 | 0.164 | 0.191 | -0.236 | 0.091 | 0.127 | 0.045 | 0.236 | 0.418 | 0.082 | 0.227 | 0.073 | 0.264 | 0.227 | 0.336 | 0.200 | 0.418 | 0.455 | 0.436 | 0.236 | 0.655 | 0.627 | 0.609 | 0.345 | 0.245 | 0.727 | 0.636 | 0.600 | 0.318 | 0.400 | 0.773 | 0.727 | 0.464 | 0.745 | 0.082 | 0.327 | 0.291 | 0.418 | 0.800 | 0.718 | 0.673 | 0.691 | 0.418 | 0.700 | 0.564 | 0.373 | 0.345 | 0.373 | 0.809 | 0.536 | 0.282 | 0.391 | NA | NA | NA | NA | NA | NA | NA |
| 572.2105_4.512 | -0.564 | -0.564 | -0.336 | -0.045 | -0.255 | -0.600 | -0.064 | -0.118 | -0.018 | 0.009 | -0.273 | -0.036 | 0.236 | 0.082 | 0.218 | -0.055 | 0.136 | 0.600 | 0.127 | 0.364 | -0.055 | 0.036 | 0.691 | 0.409 | 0.164 | 0.318 | 0.291 | 0.400 | 0.282 | 0.309 | 0.391 | 0.436 | 0.545 | 0.227 | 0.100 | 0.427 | 0.445 | 0.645 | 0.527 | 0.682 | 0.591 | 0.455 | 0.482 | 0.336 | 0.655 | 0.682 | 0.618 | 0.400 | 0.755 | 0.618 | 0.918 | 0.409 | 0.518 | 0.873 | 0.909 | 0.445 | NA | NA | NA | NA | NA | NA |
| 429.082_3.756 | -0.464 | -0.473 | -0.573 | -0.391 | 0.018 | 0.064 | -0.355 | 0.082 | 0.091 | 0.255 | 0.164 | 0.300 | 0.091 | 0.427 | 0.100 | 0.400 | 0.227 | 0.309 | 0.391 | 0.418 | 0.564 | 0.618 | 0.300 | 0.427 | 0.509 | 0.418 | 0.273 | 0.209 | 0.573 | 0.645 | 0.500 | 0.273 | 0.491 | 0.682 | 0.800 | 0.409 | 0.818 | 0.191 | 0.264 | 0.409 | 0.455 | 0.645 | 0.673 | 0.673 | 0.536 | 0.545 | 0.491 | 0.573 | 0.473 | 0.436 | 0.400 | 0.891 | 0.609 | 0.373 | 0.418 | 0.927 | 0.455 | NA | NA | NA | NA | NA |
| 413.1426_4.51 | -0.682 | -0.682 | -0.382 | -0.209 | -0.355 | -0.627 | -0.127 | 0.000 | 0.118 | 0.064 | -0.291 | 0.055 | -0.045 | 0.091 | -0.009 | -0.100 | -0.082 | 0.609 | 0.191 | 0.491 | -0.018 | 0.109 | 0.473 | 0.391 | 0.182 | 0.373 | 0.227 | 0.318 | 0.245 | 0.418 | 0.400 | 0.291 | 0.636 | 0.300 | 0.127 | 0.218 | 0.400 | 0.782 | 0.491 | 0.536 | 0.409 | 0.373 | 0.264 | 0.255 | 0.564 | 0.727 | 0.545 | 0.364 | 0.655 | 0.718 | 0.973 | 0.464 | 0.627 | 0.900 | 0.991 | 0.455 | 0.918 | 0.482 | NA | NA | NA | NA |
| 361.1499_3.066 | -0.436 | -0.682 | -0.436 | -0.018 | -0.136 | -0.418 | -0.064 | 0.318 | 0.382 | -0.082 | -0.155 | -0.164 | -0.027 | 0.227 | 0.027 | 0.164 | 0.073 | 0.464 | 0.318 | 0.245 | 0.345 | 0.345 | 0.500 | 0.191 | 0.027 | 0.455 | 0.245 | 0.473 | 0.482 | 0.436 | 0.600 | 0.482 | 0.500 | 0.227 | 0.318 | 0.445 | 0.400 | 0.736 | 0.409 | 0.718 | 0.655 | 0.482 | 0.336 | 0.436 | 0.491 | 0.582 | 0.500 | 0.591 | 0.782 | 0.718 | 0.909 | 0.618 | 0.700 | 0.836 | 0.882 | 0.527 | 0.827 | 0.591 | 0.864 | NA | NA | NA |
| 429.0815_4.032 | -0.564 | -0.418 | -0.518 | -0.418 | -0.145 | -0.018 | -0.364 | -0.127 | -0.100 | 0.291 | 0.182 | 0.355 | 0.200 | 0.373 | 0.264 | 0.391 | 0.291 | 0.291 | 0.418 | 0.455 | 0.500 | 0.582 | 0.355 | 0.491 | 0.645 | 0.400 | 0.418 | 0.282 | 0.509 | 0.564 | 0.436 | 0.318 | 0.564 | 0.700 | 0.791 | 0.473 | 0.782 | 0.245 | 0.400 | 0.418 | 0.455 | 0.636 | 0.736 | 0.736 | 0.618 | 0.645 | 0.555 | 0.573 | 0.482 | 0.527 | 0.427 | 0.864 | 0.618 | 0.473 | 0.464 | 0.900 | 0.536 | 0.964 | 0.536 | 0.555 | NA | NA |
| 333.0066_3.893 | -0.491 | -0.527 | -0.500 | -0.382 | 0.000 | -0.018 | -0.291 | -0.045 | -0.009 | 0.173 | 0.127 | 0.373 | 0.118 | 0.309 | 0.145 | 0.264 | 0.218 | 0.391 | 0.291 | 0.464 | 0.391 | 0.473 | 0.400 | 0.591 | 0.591 | 0.473 | 0.355 | 0.245 | 0.582 | 0.600 | 0.491 | 0.300 | 0.500 | 0.718 | 0.691 | 0.464 | 0.818 | 0.200 | 0.400 | 0.409 | 0.436 | 0.718 | 0.727 | 0.682 | 0.736 | 0.564 | 0.691 | 0.536 | 0.491 | 0.464 | 0.482 | 0.845 | 0.591 | 0.436 | 0.509 | 0.964 | 0.591 | 0.955 | 0.582 | 0.600 | 0.964 | NA |
Look for strongly correlated outcomes. R^2 >= to 0.5
lycsoy_red_sigcorr_metabsP05_FC1.6_immunodata <- lycsoy_red_corr_results_metabs_immuno_diff_noOutlier %>%
dplyr::select(term, il_5, il_12p70, gm_csf, total_lyc, CD45ROpos_CD45RAneg_EMCD8, CD19posCD3neg_Bcells, CD27neg_IgDpos_naiveBcells) %>%
filter(abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$il_5) >= 0.5 |
abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$il_12p70) >= 0.5 |
abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$gm_csf) >= 0.5 |
abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$total_lyc) >= 0.5 |
abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$CD45ROpos_CD45RAneg_EMCD8) >= 0.5 |
abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$CD19posCD3neg_Bcells) >= 0.5 |
abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$CD27neg_IgDpos_naiveBcells) >= 0.5 |
abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$CD16negNKcells) >= 0.5 |
abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$CD14posMDSCs) >= 0.5)
kable(lycsoy_red_sigcorr_metabsP05_FC1.6_immunodata, digits = 3, caption = "sig metabolites in lyc/soy effect list and sig immuno outcomes with strong correlations. R^2 >= to 0.5. Based on Pre vs. post-red log2fc")| term | il_5 | il_12p70 | gm_csf | total_lyc | CD45ROpos_CD45RAneg_EMCD8 | CD19posCD3neg_Bcells | CD27neg_IgDpos_naiveBcells |
|---|---|---|---|---|---|---|---|
| il_5 | NA | 0.573 | NA | NA | NA | NA | NA |
| CD16negNKcells | 0.655 | 0.500 | NA | NA | NA | NA | NA |
| CD45ROpos_CD45RAneg_EMCD8 | 0.127 | 0.527 | NA | NA | NA | NA | NA |
| gm_csf | -0.064 | 0.382 | NA | NA | 0.618 | NA | NA |
| CD19posCD3neg_Bcells | 0.073 | 0.255 | 0.200 | 0.236 | 0.500 | NA | NA |
| CD27neg_IgDpos_naiveBcells | -0.036 | 0.109 | 0.200 | 0.300 | 0.473 | 0.973 | NA |
| 605.1126_3.023 | -0.136 | -0.609 | -0.045 | -0.155 | -0.073 | -0.082 | -0.055 |
| 447.0928_3.777 | -0.027 | -0.073 | 0.745 | 0.264 | 0.282 | 0.145 | 0.200 |
| 255.0674_5.078 | -0.073 | -0.273 | 0.436 | -0.418 | 0.291 | -0.245 | -0.155 |
| 393.0495_3.605 | -0.164 | -0.236 | 0.200 | 0.145 | -0.218 | -0.500 | -0.445 |
| 217.0172_4.4 | -0.382 | -0.127 | 0.509 | 0.082 | 0.173 | -0.245 | -0.173 |
| 576.1384_0.705 | -0.100 | -0.255 | -0.291 | -0.491 | 0.318 | 0.118 | 0.191 |
| 445.0782_4.484 | -0.209 | -0.509 | 0.018 | 0.109 | -0.282 | 0.073 | 0.082 |
| 257.0816_6.056 | -0.227 | -0.427 | -0.064 | -0.727 | 0.100 | -0.191 | -0.100 |
| 335.0244_4.36 | -0.209 | -0.300 | 0.209 | -0.145 | 0.255 | -0.100 | 0.036 |
| 431.0979_3.907 | -0.109 | -0.364 | 0.345 | -0.245 | -0.091 | -0.300 | -0.218 |
| 1122.0249_4.032 | -0.109 | 0.027 | 0.191 | -0.064 | 0.145 | 0.355 | 0.418 |
| 365.0863_4.095 | -0.245 | -0.327 | 0.091 | 0.536 | -0.218 | 0.109 | 0.209 |
| 201.0227_4.086 | -0.218 | 0.136 | 0.391 | -0.109 | 0.373 | 0.400 | 0.427 |
| 509.0409_3.291 | -0.609 | -0.536 | 0.045 | 0.155 | 0.055 | 0.400 | 0.491 |
| 1122.0248_4.125 | -0.218 | 0.091 | 0.182 | -0.264 | 0.273 | 0.418 | 0.455 |
| 617.1836_4.515 | -0.282 | -0.255 | 0.127 | 0.527 | -0.064 | 0.209 | 0.300 |
| 433.1136_4.519 | 0.027 | -0.409 | -0.464 | -0.564 | -0.309 | 0.018 | 0.027 |
| 253.0503_5.048 | -0.436 | -0.336 | 0.409 | -0.500 | 0.218 | -0.164 | -0.073 |
| 413.1436_3.2 | -0.327 | -0.636 | -0.455 | -0.009 | -0.436 | 0.055 | 0.182 |
| 449.1074_4.301 | -0.100 | -0.582 | -0.182 | 0.482 | -0.364 | -0.109 | 0.000 |
| 427.197_4.049 | -0.573 | -0.418 | -0.345 | 0.309 | -0.282 | 0.000 | 0.045 |
| 201.0228_4.555 | -0.309 | -0.155 | 0.173 | 0.145 | 0.164 | 0.245 | 0.300 |
| 433.113_4.886 | -0.145 | -0.609 | -0.555 | -0.382 | -0.455 | -0.073 | -0.036 |
| 427.197_3.64 | -0.627 | -0.536 | -0.391 | 0.309 | -0.300 | 0.027 | 0.100 |
| 691.3179_4.171 | -0.300 | -0.555 | -0.445 | -0.064 | -0.645 | -0.145 | -0.091 |
| 413.1426_4.219 | -0.364 | -0.573 | -0.591 | -0.082 | -0.245 | 0.127 | 0.245 |
| 639.0783_3.758 | -0.509 | -0.436 | 0.091 | -0.300 | -0.036 | 0.045 | 0.055 |
| 345.1933_4.201 | -0.645 | -0.718 | -0.118 | 0.245 | -0.364 | 0.109 | 0.218 |
| 373.1141_3.685 | -0.227 | -0.636 | -0.636 | -0.045 | -0.509 | -0.018 | 0.073 |
| 345.1554_4.506 | -0.345 | -0.618 | -0.618 | -0.136 | -0.318 | 0.055 | 0.173 |
| 333.0047_4.33 | -0.473 | -0.336 | 0.191 | -0.236 | 0.164 | 0.091 | 0.127 |
| 572.2105_4.512 | -0.336 | -0.564 | -0.600 | -0.064 | -0.255 | -0.118 | -0.018 |
| 429.082_3.756 | -0.573 | -0.464 | 0.064 | -0.355 | 0.018 | 0.082 | 0.091 |
| 413.1426_4.51 | -0.382 | -0.682 | -0.627 | -0.127 | -0.355 | 0.000 | 0.118 |
| 361.1499_3.066 | -0.436 | -0.436 | -0.418 | -0.064 | -0.136 | 0.318 | 0.382 |
| 429.0815_4.032 | -0.518 | -0.564 | -0.018 | -0.364 | -0.145 | -0.127 | -0.100 |
| 333.0066_3.893 | -0.500 | -0.491 | -0.018 | -0.291 | 0.000 | -0.045 | -0.009 |
plot
lycsoy_red_ggcorr_noOutlier <- round(cor(lyc_soy_forcorr_diff_red_metabslog2_immuno_noOutlier[,-c(1:2)], method = "spearman"), digits = 2)
(lyc_soy_immuno_metabs_ggcorrplot <- lycsoy_red_ggcorr_noOutlier %>%
ggcorrplot(lab = TRUE,
outline.color = "white", type = "full", hc.order = FALSE,
ggtheme = ggthemes::theme_clean(base_size = 8, base_family = "sans"),
colors = c("#6D9EC1", "white", "#E46726")) +
theme(axis.text.x = element_text(angle = 90, size = 12),
axis.text.y = element_text(size = 12)))
Export corplot
ggsave(plot = lyc_soy_immuno_metabs_ggcorrplot,
filename = "plots and figures/prepostRedFC-C18-Neg-lycsoy-Metabs-and-immunology-corrplot.svg",
width = 45,
height = 40,
scale = 1)export a df for isoflavones analysis
---
title: "USDA Inflammation Metabolomics Data analysis"
subtitle: "Urine, C18(-) LCMS"
author: "Maria Sholola"
date: '2023-06-05'
output: 
  html_document:
    highlight: kate
    theme: yeti
    toc: true
    toc_float: true
    toc_depth: 5
    code_download: true
    fig_width: 7
editor_options: 
  chunk_output_type: inline
---

```{r setup, include=FALSE} 
knitr::opts_chunk$set(warning = FALSE, message = FALSE, echo=TRUE) 
```

# Load libraries
```{r, warning = FALSE, message = FALSE}
library(tidyverse) # for everything
library(readxl) # for reading in excel files
library(janitor) # data checks and cleaning
library(glue) # for easy pasting
library(FactoMineR) # for PCA
library(factoextra) # for PCA
library(rstatix) # for stats
library(pheatmap) # for heatmaps
library(plotly) # for interactive plots
library(htmlwidgets) # for saving interactive plots
library(devtools)
library(notame) # used for feature clustering
library(doParallel)
library(igraph) # feature clustering
library(ggpubr) # visualizations
library(knitr) # clean table printing
library(rmarkdown)
library(mixOmics) # for multilevel PCAs
library(corrr)
library(ggcorrplot)
library(ggthemes)
library(ggtext)
library(PCAtools)
library(pathview) # for functional analysis and KEGG annotation
```

# Read in data
```{r}
# raw filtered metabolomics data in C18 (-)
omicsdata <- read_csv("Feature lists/C18Neg-Data-Filtered-in-R.csv")

# metadata
metadata <- read_excel("Metadata-urine-c18neg.xlsx")
```

# Wrangle data

```{r}
metadata <- metadata %>%
  rename("sample_ID" = Sample_ID)
```


```{r}
# rename "row ID"
omicsdata <- omicsdata %>%
  rename("row_ID" = `row ID`)

# how many features
nrow(omicsdata)

# are there any duplicates?
omicsdata %>% get_dupes(mz_rt)

```

```{r}
# remove dupes
omicsdata <- omicsdata %>% 
  distinct(mz_rt, .keep_all = TRUE)

# check again for dupes
omicsdata %>% get_dupes(mz_rt)

# how many features
nrow(omicsdata)
```


Sometimes a weird logical column (lgl) comes up in my data. Let's check if it's there

```{r}
colnames(omicsdata)
```


```{r}
# remove weird lgl column
omicsdata <- omicsdata %>%
  dplyr::select(!where(is.logical)) 

colnames(omicsdata)
```



```{r}
# create long df for omics df
omicsdata_tidy <- omicsdata %>%
  pivot_longer(cols = 3:ncol(.),
               names_to = "sample_ID",
               values_to = "peak_height")

# combine meta and omics dfs
meta_omics <- full_join(omicsdata_tidy,
                         metadata,
                         by = c("sample_ID" = "sample_ID"))

# separate mz and rt
meta_omics_sep <- meta_omics %>%
  separate(col = mz_rt,
           into = c("mz", "rt"),
           sep = "_") 

# convert columns to correct type
meta_omics_sep$mz <- as.numeric(meta_omics_sep$mz)
meta_omics_sep$rt <- as.numeric(meta_omics_sep$rt)
meta_omics_sep$Subject <- as.character(meta_omics_sep$Subject)
meta_omics_sep$Intervention <- as.character(meta_omics_sep$Intervention)

# rearrange column order
meta_omics_sep <- meta_omics_sep %>%
  dplyr::select(sample_ID, pre_post, Intervention, everything())

str(meta_omics_sep)


```


```{r}
# replace NA's in subject and intervention columns with QC
meta_omics_sep$Subject <- meta_omics_sep$Subject %>%
  replace_na("QC")

meta_omics_sep$Intervention <- meta_omics_sep$Intervention %>%
  replace_na("QC")
```


# Data summaries

## Number of masses detected
```{r}
nrow(omicsdata)
```


## Mass range for metabolites detected?

```{r}
range(meta_omics_sep$mz)
```

## RT range for metabolites detected?

```{r}
range(meta_omics_sep$rt)
```

## mass vs RT scatterplot
```{r}
# plot
(plot_mzvsrt <- meta_omics_sep %>%
  ggplot(aes(x = rt, y = mz)) +
  geom_point() +
  theme_minimal() +
  labs(x = "Retention time, min",
       y = "m/z, neutral",
       title = "mz across RT for all features"))
```

## Histogram for mass range
```{r}
meta_omics_sep %>%
  ggplot(aes(x = mz)) +
  geom_histogram(binwidth = 25) +
  theme_minimal() +
  labs(x = "Monoisotopic mass (amu)",
       y = "Number of features",
       title = "Distribution of features by mass")
```

## Histogram for RT

```{r}
meta_omics_sep %>%
  ggplot(aes(x = rt)) +
  geom_histogram(binwidth = 0.1) + # 6 second bins
  theme_minimal() +
  labs(x = "Retention time",
       y = "Number of features",
       title = "Distribution of features by retention time")
```


# NAs and imputing

## NAs

```{r}
# samples only (no QCs)
omicsdata_noQC <- omicsdata %>%
  dplyr::select(-contains("QC"))

#NAs in samples only?
NAbyRow_noQC <- rowSums(is.na(omicsdata_noQC[,-1]))

hist(NAbyRow_noQC,
     breaks = 48, # because there are 48 samples 
     xlab = "Number of missing values",
     ylab = "Number of metabolites",
     main = "How many missing values are there?")
```

Are there any missing values in QCs? There shouldn't be after data preprocessing/filtering
```{r}
omicsdata_QC <- omicsdata %>%
  dplyr::select(starts_with("P")) 

NAbyRow_QC <- colSums(is.na(omicsdata_QC))
# lets confirm that there are no missing values from my QCs
sum(NAbyRow_QC) # no
```


```{r}
# calculate how many NAs there are per feature in whole data set
contains_NAs <- meta_omics %>%
  group_by(mz_rt) %>%
  count(is.na(peak_height)) %>%
  filter(`is.na(peak_height)` == TRUE)
kable(contains_NAs)
```


## Remove NAs


## Data imputation
```{r}
# impute any missing values by replacing them with 1/2 of the lowest peak height value of a feature (i.e. in a row).
imputed_omicsdata <- omicsdata

imputed_omicsdata[] <- lapply(imputed_omicsdata, 
                              function(x) ifelse(is.na(x),
                                                 min(x, na.rm = TRUE)/2, x))

dim(imputed_omicsdata)
```

Are there any NAs?
```{r}
imputed_omicsdata %>%
  is.na() %>%
  sum()

# imputations worked
```


# Create new imputed tidy datasets
```{r}
# create long df for imputed omics df
imputed_omicsdata_tidy <- imputed_omicsdata %>%
  pivot_longer(cols = 3:ncol(.),
               names_to = "sample_ID",
               values_to = "peak_height")

# combine meta and imputed omics dfs
imp_meta_omics <- full_join(imputed_omicsdata_tidy,
                         metadata,
                         by = c("sample_ID" = "sample_ID"))

# separate mz and rt
imp_meta_omics_sep <- imp_meta_omics %>%
  separate(col = mz_rt,
           into = c("mz", "rt"),
           sep = "_") 

# convert columns to correct type
imp_meta_omics_sep$mz <- as.numeric(imp_meta_omics_sep$mz)
imp_meta_omics_sep$rt <- as.numeric(imp_meta_omics_sep$rt)
imp_meta_omics_sep$Subject <- as.character(imp_meta_omics_sep$Subject)
imp_meta_omics_sep$Intervention <- as.character(imp_meta_omics_sep$Intervention)
```


# Notame feature reduction
vignette for reference
```{r}
#browseVignettes("notame")
```

Let's look at what masses come up at each RT again

```{r}
# rt vs mz plot
imp_meta_omics_sep %>%
  ggplot(aes(x = rt, y = mz)) +
  geom_point() +
  theme_minimal() +
  labs(x = "RT (min)",
       y = "mz")
```
There are several points that are at the same RT, meaning they could be coming from the same compound. We'll run notame clustering to collapse features coming from one mass into one feature.

## Data restructuring for notame
```{r}
# create features list from imputed data set to only include unique feature ID's (mz_rt), mz and RT
features <- imp_meta_omics_sep %>%
  cbind(imp_meta_omics$mz_rt) %>%
  rename("mz_rt" = "imp_meta_omics$mz_rt") %>%
  dplyr::select(c(mz_rt, mz, rt)) %>%
  distinct() # remove the duplicate rows

# create a second data frame which is just imp_meta_omics restructured to another wide format
data_notame <- data.frame(imputed_omicsdata %>%
                            dplyr::select(-row_ID) %>%
                            t())

data_notame <- data_notame %>%
  tibble::rownames_to_column() %>% # change samples from rownames to its own column
  row_to_names(row_number = 1) # change the feature IDs (mz_rt) from first row obs into column names


```

Check structures
```{r}
# check if mz and rt are numeric
str(features)
```

```{r}
# check if results are numeric
head(data_notame)

# change to results to numeric
data_notame <- data_notame %>%
  mutate_at(-1, as.numeric)

head(data_notame)
```


## Find connections
```{r}
connection <- find_connections(data = data_notame,
                               features = features,
                               corr_thresh = 0.9,
                               rt_window = 1/60,
                               name_col = "mz_rt",
                               mz_col = "mz",
                               rt_col = "rt")

head(connection)
```

## Clustering
```{r}
clusters <- find_clusters(connections = connection, d_thresh = 0.8)
```

```{r}
# assign a cluster ID to all features. Clusters are named after feature with highest median peak height
features_clustered <- assign_cluster_id(data_notame, clusters, features, name_col = "mz_rt")

# lets see how many features are removed when we only keep one feature per cluster
pulled <- pull_clusters(data_notame, features_clustered, name_col = "mz_rt")
cluster_data <- pulled$cdata
cluster_features <- pulled$cfeatures

# how much did we trim our data down by?
nrow(omicsdata) - nrow(cluster_features)

```


```{r}
# export clustered feature list
write_csv(cluster_features,
          "Feature lists/c18neg-clustered-features.csv")

```


## Reduce dataset based on clustering
 
```{r}
# transpose the full dataset back to wide so that it is more similar to the notame dataset
imp_meta_omics_wide <- imp_meta_omics %>%
  dplyr::select(-"row_ID") %>%
  pivot_wider(names_from = mz_rt,
              values_from = peak_height)

# list of reduced features
clusternames <- cluster_features$mz_rt

# select only the features are in the reduced list
imp_clust <- imp_meta_omics_wide[,c(names(imp_meta_omics_wide) %in% clusternames)]

# bind back sample names
imp_clust <- cbind(imp_meta_omics_wide[1], imp_clust)

tibble(imp_clust)

```

## New mz vs rt plot

Let's see how our clustered data looks now compared to the original

```{r}
# plot new rt vs mz scatterplot post-clustering
(plot_mzvsrt_postcluster <- cluster_features %>%
  ggplot(aes(x = rt,
             y = mz)) +
  geom_point() +
  theme_minimal() +
  labs(x = "Retention time, min",
       y = "m/z, neutral",
       title = "mz across RT for all features after clustering"))


```


```{r, fig.height=10, fig.asp=1}
# plot both scatterplots to compare with and without notame clustering
(scatterplots <- ggarrange(plot_mzvsrt, 
                           plot_mzvsrt_postcluster, 
                           nrow = 2))
```

There are still some features that line up, but the clustered data looks much better.

# Wrangle new data

```{r}
# bind back new clustered data with metadata
imp_metabind_clust <- right_join(metadata, 
                                 imp_clust,
                                 by = "sample_ID")
```

# Visualize untransformed data

## More wrangling
```{r}
# change meta data columns to character so that I can change NAs from QCs to "QC"
imp_metabind_clust <- imp_metabind_clust %>%
  mutate_at(c("Subject",
              "Period",
              "Intervention",
              "pre_post",
              "sequence",
              "Intervention_week",
              "Sex",
              "Age",
              "BMI"),
            as.character) 

# replace NAs in metadata columns for QCs
imp_metabind_clust[is.na(imp_metabind_clust)] <- "QC"

# unite pre_post column with intervention column to create pre_intervention column
imp_metabind_clust <- imp_metabind_clust %>%
  unite(col = "pre_post_intervention",
        c("pre_post","Intervention"),
        sep = "_",
        remove = FALSE)

# long df
imp_metabind_clust_tidy <- imp_metabind_clust %>%
  pivot_longer(cols = 12:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund")

# structure check
str(imp_metabind_clust_tidy)
```


## Boxplot
```{r}
imp_metabind_clust_tidy %>%
  ggplot(aes(x = sample_ID, y = rel_abund, color = Intervention)) +
  geom_boxplot(alpha = 0.6) +
  scale_color_manual(values = c("light grey", "tomato1", "gold")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "LC-MS (-) Feature Abundances by Sample",
       subtitle = "Unscaled data",
       y = "Relative abundance")
```

Will need to log transform in order to normalize and actually see the data

# Log2 transform
```{r}
imp_metabind_clust_tidy_log2 <- imp_metabind_clust_tidy %>%
  mutate(rel_abund_log2 = log2(rel_abund))
```

## Boxplot
```{r}
(bp_data_quality <- imp_metabind_clust_tidy_log2 %>%
  ggplot(aes(x = sample_ID, y = rel_abund_log2, fill = Intervention)) +
  geom_boxplot(alpha = 0.6) +
  scale_fill_manual(values = c("light grey", "tomato1", "gold")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "LC-MS (-) Feature Abundances by Sample",
       subtitle = "Log2 transformed data",
       y = "Relative abundance"))
```

Much better! QCs look good.

# PCAs

## With QCS

### Wrangle

```{r}
# go back to wide data
imp_metabind_clust_log2 <- imp_metabind_clust_tidy_log2 %>%
  dplyr::select(!rel_abund) %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2)

PCA.imp_metabind_clust_log2 <- PCA(imp_metabind_clust_log2,  # wide data
                                   quali.sup = 1:11, # remove qualitative variables
                                   graph = FALSE, # don't graph
                                   scale.unit = FALSE) # don't scale, already transformed data

# PCA summary
kable(summary(PCA.imp_metabind_clust_log2))
```

```{r}
# pull PC coordinates into df
PC_coord_QC_log2 <- as.data.frame(PCA.imp_metabind_clust_log2$ind$coord)

# bind back metadata from cols 1-10
PC_coord_QC_log2 <- bind_cols(imp_metabind_clust_log2[,1:11], PC_coord_QC_log2)

# grab some variance explained
importance_QC <- PCA.imp_metabind_clust_log2$eig

# set variance explained with PC1, round to 2 digits
PC1_withQC <- round(importance_QC[1,2], 2)

# set variance explained with PC2, round to 2 digits
PC2_withQC <- round(importance_QC[2,2], 2)
```

### Plots
Using FactoExtra package
```{r}
# scree plot
fviz_eig(PCA.imp_metabind_clust_log2)

# get eigenvalues
kable(get_eig(PCA.imp_metabind_clust_log2))
```

```{r}
# scores plot
fviz_pca_ind(PCA.imp_metabind_clust_log2)
```


### Manual scores plot

```{r}
# manual scores plot
(PCA_withQCs <- PC_coord_QC_log2 %>%
  ggplot(aes(x = Dim.1, y = Dim.2,
             fill = factor(Intervention, levels = c("Yellow", "Red", "QC")))) +
  geom_point(shape = 21, alpha = 0.8) +
  scale_fill_manual(values = c("gold", "tomato1", "light grey")) +
  scale_color_manual(values = "black") +  
  theme_minimal() +
  coord_fixed(PC2_withQC/PC1_withQC) +
  labs(x = glue::glue("PC1: {PC1_withQC}%"),
       y = glue::glue("PC2: {PC2_withQC}%"),
       fill = "Group",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data"))
```

There seems to be outliers here. We'll keep investigating. Let's see how the data looks without the QCs.

## Without QCs

### Wrangle 

```{r}
imp_metabind_clust_log2_noQCs <- imp_metabind_clust_log2 %>%
  filter(Intervention != "QC")

PCA.imp_metabind_clust_log2_noQCs <- PCA(imp_metabind_clust_log2_noQCs, # wide data
                               quali.sup=1:11, # remove qualitative variables
                               graph=FALSE, # don't graph
                               scale.unit=FALSE) # don't scale, we already did this

# look at summary
kable(summary(PCA.imp_metabind_clust_log2_noQCs))
```

```{r}
# pull PC coordinates into df
PC_coord_noQCs_log2 <- as.data.frame(PCA.imp_metabind_clust_log2_noQCs$ind$coord)

# bind back metadata from cols 1-10
PC_coord_noQCs_log2 <- bind_cols(imp_metabind_clust_log2_noQCs[,1:11], PC_coord_noQCs_log2)

# grab some variance explained
importance_noQC <- PCA.imp_metabind_clust_log2_noQCs$eig

# set variance explained with PC1, round to 2 digits
PC1_noQC <- round(importance_noQC[1,2], 2)

# set variance explained with PC2, round to 2 digits
PC2_noQC <- round(importance_noQC[2,2], 2)
```

### Plots
Using FactoExtra

```{r}
# scree plot
fviz_eig(PCA.imp_metabind_clust_log2_noQCs)
```

```{r}
# scores plot
fviz_pca_ind(PCA.imp_metabind_clust_log2_noQCs)
```


### Manual scores plot

```{r}
(PCA_withoutQCs <- PC_coord_noQCs_log2 %>%
  ggplot(aes(x = Dim.1, 
             y = Dim.2,
             fill = Intervention,
             text = sample_ID)) +
    geom_point(shape = 21, alpha = 0.8) +
    geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
    geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
    scale_fill_manual(values = c("gold", "tomato1")) +
    scale_color_manual(values = "black") +  
    theme_minimal() +
    coord_fixed(PC2_noQC/PC1_noQC) +
    labs(x = glue::glue("PC1: {PC1_noQC}%"),
         y = glue::glue("PC2: {PC2_noQC}%"),
         fill = "Intervention",
         title = "Principal Components Analysis Scores Plot",
         subtitle = "Log2 transformed data, without QCs"))
ggplotly(PCA_withoutQCs, tooltip = "text")
```

Interactive plot shows us that all of those visual outliers are subject 6106. Let's see what the data looks like when we remove this outlier.

## Removal of outlier

### With QCs

#### Wrangle

```{r}
# go back to wide data
imp_no6106_log2 <- imp_metabind_clust_tidy_log2 %>%
  dplyr::select(!rel_abund) %>%
  filter(Subject != 6106) %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2)

PCA.imp_no6106_log2 <- PCA(imp_no6106_log2,  # wide data
                                   quali.sup = 1:11, # remove qualitative variables
                                   graph = FALSE, # don't graph
                                   scale.unit = FALSE) # don't scale, already transformed data

# PCA summary
kable(summary(PCA.imp_no6106_log2))
```

```{r}
# pull PC coordinates into df
PC_no6106_QC_log2 <- as.data.frame(PCA.imp_no6106_log2$ind$coord)

# bind back metadata from cols 1-11
PC_no6106_QC_log2 <- bind_cols(imp_no6106_log2[,1:11], PC_no6106_QC_log2)

# grab some variance explained
importance_no6106_QC <- PCA.imp_no6106_log2$eig

# set variance explained with PC1, round to 2 digits
PC1_no6106_withQC <- round(importance_no6106_QC[1,2], 2)

# set variance explained with PC2, round to 2 digits
PC2_no6106_withQC <- round(importance_no6106_QC[2,2], 2)
```

#### Plots
Using FactoExtra package
```{r}
# scree plot
fviz_eig(PCA.imp_no6106_log2)
```

```{r}
# scores plot
fviz_pca_ind(PCA.imp_no6106_log2)
```


#### Manual scores plot

```{r}
# manual scores plot
(PCA_no6106_withQCs <- PC_no6106_QC_log2 %>%
  ggplot(aes(x = Dim.1, y = Dim.2,
             fill = factor(Intervention, levels = c("Yellow", "Red", "QC")))) +
  geom_point(shape = 21, alpha = 0.8) +
  scale_fill_manual(values = c("gold", "tomato1", "light grey")) +
  scale_color_manual(values = "black") +  
  theme_minimal() +
  coord_fixed(PC2_no6106_withQC/PC1_no6106_withQC) +
  labs(x = glue::glue("PC1: {PC1_no6106_withQC}%"),
       y = glue::glue("PC2: {PC2_no6106_withQC}%"),
       fill = "Group",
       title = "Principal Components Analysis Scores Plot"))
```


Because of subject 6106, the pooled QC sample points are quite far from samples. 

### Without QCs

#### Wrangle
```{r}
imp_no6106_log2_noQCs <- imp_metabind_clust_log2 %>%
  filter(Intervention != "QC") %>%
  filter(Subject != "6106")

PCA.imp_no6106_log2_noQCs <- PCA(imp_no6106_log2_noQCs, # wide data
                               quali.sup=1:11, # remove qualitative variables
                               graph=FALSE, # don't graph
                               scale.unit=FALSE) # don't scale, we already did this

# look at summary
kable(summary(PCA.imp_no6106_log2_noQCs))
```

```{r}
# pull PC coordinates into df
PC_coord_no6106_noQC_log2 <- as.data.frame(PCA.imp_no6106_log2_noQCs$ind$coord)

# bind back metadata from cols 1-11
PC_coord_no6106_noQC_log2 <- bind_cols(imp_no6106_log2_noQCs[,1:11], PC_coord_no6106_noQC_log2)

# grab some variance explained
importance_no6106_noQC <- PCA.imp_no6106_log2_noQCs$eig

# set variance explained with PC1, round to 2 digits
PC1_no6106_noQC <- round(importance_no6106_noQC[1,2], 2)

# set variance explained with PC2, round to 2 digits
PC2_no6106_noQC <- round(importance_no6106_noQC[2,2], 2)
```


#### Plots
Using FactoExtra
```{r}
# scree plot
fviz_eig(PCA.imp_no6106_log2_noQCs)
```

```{r}
# scores plot
fviz_pca_ind(PCA.imp_no6106_log2_noQCs)
```


#### Manual scores plot

```{r}
(PCA_no6106_withoutQCs <- PC_coord_no6106_noQC_log2 %>%
  ggplot(aes(x = Dim.1, 
             y = Dim.2,
             fill = Intervention)) +
    geom_point(shape = 21, alpha = 0.8) +
    geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
    geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
    scale_fill_manual(values = c("tomato1", "gold")) +
    scale_color_manual(values = "black") +  
    theme_minimal() +
    coord_fixed(PC2_no6106_noQC/PC1_no6106_noQC) +
    labs(x = glue::glue("PC1: {PC1_no6106_noQC}%"),
         y = glue::glue("PC2: {PC2_no6106_noQC}%"),
         fill = "Intervention",
         title = "Principal Components Analysis Scores Plot"))
ggplotly(PCA_no6106_withoutQCs)
```

Now we're seeing some separation along PC2! Let's explore this more with a pub-friendly package for PCAs, the PCAtools package.


# PCAtools

## W/ Outlier

### Data wrangling
```{r}
# create rel abund df suitable for PCAtools package
imp_clust_omicsdata_outliers_forPCAtools <- as.data.frame(t(imp_clust)) # transpose df 

names(imp_clust_omicsdata_outliers_forPCAtools) <- imp_clust_omicsdata_outliers_forPCAtools[1,] # make sample IDs column names

imp_clust_omicsdata_outliers_forPCAtools <- imp_clust_omicsdata_outliers_forPCAtools[-1,] # remove sample ID row

# create metadata df suitable for PCAtools pckg

metadata_outliers_forPCAtools <- metadata %>%
  column_to_rownames(var = "sample_ID") # change sample ID column to rownames

# create a vector so that col names in abundance df matches metadata df
order_outliers_forPCAtools <- match(rownames(metadata_outliers_forPCAtools), colnames(imp_clust_omicsdata_outliers_forPCAtools))

# reorder col names in abundance df so that it matches metadata
abundata_outliers_reordered_forPCAtools <- imp_clust_omicsdata_outliers_forPCAtools[ ,order_outliers_forPCAtools] 

# change abundance df to numeric
abundata_outliers_reordered_forPCAtools <- abundata_outliers_reordered_forPCAtools %>%
  mutate_all(as.numeric)

# Log transform
log2_abundata_outliers_forPCAtools <- log2(abundata_outliers_reordered_forPCAtools)


# unite pre_post column with intervention column to create pre_intervention column
metadata_outliers_forPCAtools <- metadata_outliers_forPCAtools %>%
  unite(col = "pre_post_intervention",
        c("pre_post","Intervention"),
        sep = "_",
        remove = FALSE)
```


### PCA

```{r, fig.width=10, fig.height=6.5, fig.asp=0.5}
# pca
p_outliers <- PCAtools::pca(log2_abundata_outliers_forPCAtools, 
         metadata = metadata_outliers_forPCAtools, 
         scale = FALSE # using scaled data already (log2 transformed)
         
)

(PCAtools_outliers <- biplot(p_outliers,
                               lab = paste0(metadata_outliers_forPCAtools$Subject),
                           colby = 'pre_post_intervention',
                           colkey = c("pre_Yellow" = "yellow",
                                      "post_Yellow" = "yellow4",
                                      "pre_Red" = "red",
                                      "post_Red" = "red4"),
                           hline = 0, vline = 0,
                           legendPosition = 'right',
                           title = "PCA Scores Plot with Loadings",
                           subtitle = "Log2 transformed data, C18 (-), without QCs but with outliers",
                           ellipse = TRUE,
                           ellipseType = 't', # assumes multivariate
                           ellipseLevel = 0.95,
                           ellipseFill = TRUE,
                           ellipseAlpha = 0.2,
                           ellipseLineSize = 0,
                           showLoadings = TRUE))

```


We see here the top 5 metabolites driving separation along PC1, which is really separation between subject 6106 and the rest of the samples. These metabolites are worth me exploring a bit more as to why subject 6106 is an outlier. Let's move on and explore the data without outliers more. Confidence ellipses were added to show that 6106 samples are statistical outliers.

## No outliers

### Data wrangling
```{r}
# create rel abund df suitable for PCAtools package

imp_clust_omicsdata_forPCAtools <- as.data.frame(t(imp_clust)) # transpose df 

names(imp_clust_omicsdata_forPCAtools) <- imp_clust_omicsdata_forPCAtools[1,] # make sample IDs column names

imp_clust_omicsdata_forPCAtools <- imp_clust_omicsdata_forPCAtools[-1,] # remove sample ID row

imp_clust_omicsdata_forPCAtools <- imp_clust_omicsdata_forPCAtools %>%
  dplyr::select(!contains("QC")) # remove QC observations


# create metadata df suitable for PCAtools pckg

metadata_forPCAtools <- metadata %>%
  column_to_rownames(var = "sample_ID") # change sample ID column to rownames

# create a vector so that col names in abundance df matches metadata df
order_forPCAtools <- match(rownames(metadata_forPCAtools), colnames(imp_clust_omicsdata_forPCAtools))

# reorder col names in abundance df so that it matches metadata
abundata_reordered_forPCAtools <- imp_clust_omicsdata_forPCAtools[ ,order_forPCAtools] 

# change abundance df to numeric
abundata_reordered_forPCAtools <- abundata_reordered_forPCAtools %>%
  mutate_all(as.numeric)

# Log transform
log2_abundata_forPCAtools <- log2(abundata_reordered_forPCAtools)

# remove 6106 subj from both df
log2_abundata_forPCAtools <- log2_abundata_forPCAtools %>%
  dplyr::select(!contains("6106"))

metadata_forPCAtools <- metadata_forPCAtools %>%
  filter(Subject != 6106)

# unite pre_post column with intervention column to create pre_intervention column
metadata_forPCAtools <- metadata_forPCAtools %>%
  unite(col = "pre_post_intervention",
        c("pre_post","Intervention"),
        sep = "_",
        remove = FALSE)

```

### Screeplot

Elbow method
```{r}
# pca
p <- PCAtools::pca(log2_abundata_forPCAtools, 
         metadata = metadata_forPCAtools, 
         scale = FALSE # using scaled data already (log2 transformed)
         
)

elbow <- findElbowPoint(p$variance)

elbow
```

```{r, fig.width=7, fig.height=7.5}
  screeplot(p,
    components = getComponents(p, 1:20),
    vline = elbow) +
    geom_label(aes(x = elbow + 1, y = 50,
      label = 'Elbow method', vjust = -3, size = 8))
```

How many PCs do we need to capture at least 80% variance?
```{r}
which(cumsum(p$variance) > 80)[1]
```

This shows we'd need quite a few PCs to capture most of the variance. Let's move on with 2 PCs and explore more later.

### PCA plots

#### w/ stats ellipses

```{r, fig.width=10, fig.height=10}
PCAtools::biplot(p,
       lab = paste0(metadata_forPCAtools$Subject),
       colby = 'pre_post_intervention',
       colkey = c("pre_Yellow" = "yellow",
                  "post_Yellow" = "yellow4",
                  "pre_Red" = "red",
                  "post_Red" = "red4"),
       hline = 0, vline = 0,
       # ellipse config
       ellipse = TRUE,
       ellipseType = 't', # assumes multivariate t-distribution
       ellipseLevel = 0.95,
       ellipseFill = TRUE,
       ellipseAlpha = 0.2,
       ellipseLineSize = 0,
       xlim = c(-80,80), ylim = c(-40,40),
       legendPosition = 'right',
       title = "PCA Scores Plot",
       subtitle = "Log2 transformed data, C18 (-), outliers removed, no QCs \n95% confidence level ellipses")

```

With confidence intervals (Hotelling's t), most samples are within the intervals except for a few that are not far off. We will move forward.

#### w/ loadings
Let's look at the metabolites driving
```{r, fig.width=8.5, fig.height=6.5}
(PCA.colby.prevspost <- biplot(p,
                               lab = NULL,
                           # or lab = paste0(metadata_forPCAtools$Subject),
                           colby = 'pre_post_intervention',
                           colkey = c("pre_Yellow" = "yellow",
                                      "post_Yellow" = "yellow4",
                                      "pre_Red" = "red",
                                      "post_Red" = "red4"),
                           hline = 0, vline = 0,
                           legendPosition = 'right',
                           title = "PCA Scores Plot",
                           subtitle = "Log2 transformed data, C18 (-), without QCs and outliers",
                           ylim = c(-45,45),
                           showLoadings = TRUE))


```

Feature 201.0228 at RT 4.555 drives the most separation along PC2 according to this PCA.

### Pairs plots

Here, we will look at separations for several components at once using pairs plots.

#### post-intervention comparisons
```{r, fig.width=10, fig.height=10}
(PCA_pairsplot.prevspost <-
  pairsplot(p,
    components = getComponents(p, c(1:10)),
    triangle = TRUE, trianglelabSize = 12,
    hline = 0, vline = 0,
    pointSize = 0.4,
    gridlines.major = FALSE, gridlines.minor = FALSE,
    colby = 'pre_post_intervention', 
    colkey = c("pre_Yellow" = "yellow",
               "post_Yellow" = "yellow4",
               "pre_Red" = "pink",
               "post_Red" = "red4"),
    title = 'Pairs plot', plotaxes = FALSE,
    margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm')))

```

#### sex?
Are there any obvious clusterings when colored by sex?

```{r, fig.width=10, fig.height=10}
  pairsplot(p,
    components = getComponents(p, c(1:10)),
    triangle = TRUE, trianglelabSize = 12,
    hline = 0, vline = 0,
    pointSize = 0.4,
    gridlines.major = FALSE, gridlines.minor = FALSE,
    colby = 'Sex', 
    colkey = c("M" = "red",
               "F" = "purple"),
    title = 'Pairs plot', plotaxes = FALSE,
    margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))
```


#### sequence?

```{r, fig.width=10, fig.height=10}
  pairsplot(p,
    components = getComponents(p, c(1:10)),
    triangle = TRUE, trianglelabSize = 12,
    hline = 0, vline = 0,
    pointSize = 0.4,
    gridlines.major = FALSE, gridlines.minor = FALSE,
    colby = 'sequence',
    title = 'Pairs plot', plotaxes = FALSE,
    margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))
```

## Eigen corplots

This is a cool way to explore the correlations between the metadata and the PCs! I want to look at how the metavariables correlate with PCs that account for 80% variation in the dataset. 

Again: How many PCs do we need to capture at least 80% variance?
```{r}
which(cumsum(p$variance) > 80)[1]
```

```{r, fig.width=12, fig.height=7.5}
  eigencorplot(p,
    components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]), # get components that account for 80% variance
    metavars = colnames(metadata_forPCAtools),
    col = c('darkblue', 'blue2', 'gray', 'red2', 'darkred'),
    cexCorval = 0.7,
    colCorval = 'white',
    fontCorval = 2,
    posLab = 'bottomleft',
    rotLabX = 45,
    posColKey = 'top',
    cexLabColKey = 1.5,
    scale = TRUE,
    main = 'PC1-15 metadata correlations',
    colFrame = 'white',
    plotRsquared = FALSE)
```


```{r, fig.width=14, fig.height=7.5}
  eigencorplot(p,
    components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]),
    metavars = colnames(metadata_forPCAtools),
    col = c('white', 'cornsilk1', 'gold', 'forestgreen', 'darkgreen'),
    cexCorval = 1.2,
    fontCorval = 2,
    posLab = 'all',
    rotLabX = 45,
    scale = TRUE,
    main = bquote(Principal ~ component ~ Pearson ~ r^2 ~ metadata ~ correlates),
    plotRsquared = TRUE,
    corFUN = 'pearson',
    corUSE = 'pairwise.complete.obs',
    corMultipleTestCorrection = 'BH',
    signifSymbols = c('****', '***', '**', '*', ''),
    signifCutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1))
```


I am most interested in PCs affected by pre_post_intervention, so I think it would be good to investigate the metabolites that contribute the most to these PCs.



# Multilevel PCA

Because this is a crossover trial, measurements are taken from the same person repeatedly and thus introduces variance not dependent on the treatment but the subject. To account for interindividual variations in MVA, I try multilevel PCAs from the mixOmics package. Here, I do not remove subject 6106.

```{r}
library(mixOmics)
```


```{r}
# make df suitable for mixOmics
Data_forMPCA <- imp_metabind_clust_log2_noQCs %>%
  mutate_at("Subject", as.factor) # change subject id to factor
 

summary(as.factor(Data_forMPCA$Subject))

# make a vector for meta variables
(metavar <- Data_forMPCA[,c(1:11)] %>%
    colnames())
```

## Regular scores plot
```{r}
mixOmicsPCA.result <- mixOmics::pca(Data_forMPCA[,!names(Data_forMPCA) %in% metavar],
                            scale = FALSE,
                            center = FALSE)

plotIndiv(mixOmicsPCA.result, 
          ind.names = Data_forMPCA$Subject, 
          group = Data_forMPCA$pre_post_intervention, 
          legend = TRUE, 
          legend.title = "Treatment", 
          title = 'Regular PCA, C18 (-), Log2 transformed')

```

We see the same thing we've been seeing with 6106 overwhelming PC2. Let's try the multilevel pca and see what happens!

## Multilevel scores plot

```{r}
multilevelPCA.result <- mixOmics::pca(Data_forMPCA[,-(c(1:11))], 
                            multilevel = Data_forMPCA$Subject,
                            scale = FALSE,
                            center = FALSE)

plotIndiv(multilevelPCA.result, 
          ind.names = Data_forMPCA$Subject, 
          group = Data_forMPCA$pre_post_intervention, 
          legend = TRUE, 
          legend.title = "Treatment", 
          title = 'Multilevel PCA, C18 (-), Log2 transformed')

```

Now, even with 6106 still included, we see clear separation between post-Red and other timepoints! Let's take a look at a loadings plot to see the top 40 features that contribute to PC1 separation.

### Loadings 
```{r, fig.width=12, fig.height=12}
plotLoadings(multilevelPCA.result, ndisplay = 40)
```

Feature 201.0228 still is the top driver of separation. More digging into PCA/MVA to come!

# Univariate analysis

## Wrangle data
```{r}
# use tidy data 
head(imp_metabind_clust_tidy_log2)

# remove QCs
df_for_stats <- imp_metabind_clust_tidy_log2 %>%
  filter(Intervention != "QC")

# check if QCs were removed
unique(df_for_stats$Intervention)
```

```{r}
# df without outlier 6106
df_for_stats_noOutlier <- df_for_stats %>%
  filter(Subject != "6106")

# check if outlier was removed
unique(df_for_stats_noOutlier$Subject)
```

```{r}
# turn off sci notation outputs
options(scipen = 999)
```


## Parametric tests

## ANOVA (repeated measures) across timepoints

```{r}

anova_outpout_df <- df_for_stats_noOutlier %>% 
  dplyr::select(Subject, pre_post_intervention, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  anova_test(rel_abund_log2 ~ pre_post_intervention, wid = Subject,
             detailed = TRUE) %>%
  adjust_pvalue(method = "BH") %>%
  as.data.frame()

anova_sig <- anova_outpout_df %>%
  filter(p.adj <= .05)

# how many significant features?
nrow(anova_sig)
  
```

### Heatmap of features significant by ANOVA

```{r, fig.height=20, fig.width=12, fig.asp=1}
# go back to wide for stats df
df_for_stats_wide_noOutlier <- df_for_stats_noOutlier %>%
  dplyr::select(!rel_abund) %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2)

ANOVA_heatmap_data <- df_for_stats_wide_noOutlier %>%
  unite("Subject_pre_post_intervention", Subject, pre_post_intervention, sep = "_", remove = FALSE) %>%
  dplyr::select(Subject, Subject_pre_post_intervention, pre_post, all_of(anova_sig$mz_rt))


ANOVA_heatmap <- 
  pheatmap(ANOVA_heatmap_data[,-c(1:3)],
           scale = "column",
           cluster_rows = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           clustering_method = "ward.D2",
           labels_row = ANOVA_heatmap_data$Subject_pre_post_intervention,
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of features significant across timepoints \nby repeated measures one-way ANOVA \nBenjamoni-Hochberg corrected p-values > 0.05 \nC18 (-)")
  
```


```{r}
ggsave(plot = ANOVA_heatmap, height = 20, width = 12,
       filename = "plots and figures/ANOVA_sig_heatmap.svg")
```

### Paired t tests

Here, I am comparing pre- to post-intervention for both yellow and tomato soy (Red) juice interventions. I also compare post-yellow to post-red intervention. I am using the log transformed values of rel abundance since parametric tests assume normality.

#### Ctrl
```{r}
# run paired t-tests for control intervention
ctrl_t.test_paired <- df_for_stats %>%
  filter(Intervention == "Yellow") %>%
  dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ pre_post, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()
```

Statistically significant features
```{r}
# which features are significant?
ctrl_t.test_paired_sig <- ctrl_t.test_paired %>%
  filter(p <= 0.05)
kable(ctrl_t.test_paired_sig)

# how many are significant?
nrow(ctrl_t.test_paired_sig)
```


#### Ctrl - no outlier
```{r}
# run paired t-tests for control intervention
ctrl_noOutlier_t.test_paired <- df_for_stats_noOutlier %>%
  filter(Intervention == "Yellow") %>%
  dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ pre_post, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()
```

Statistically significant features
```{r}
# which features are significant?
ctrl_noOutlier_t.test_paired_sig <- ctrl_noOutlier_t.test_paired %>%
  filter(p <= 0.05)
kable(ctrl_noOutlier_t.test_paired_sig)

# how many are significant?
nrow(ctrl_noOutlier_t.test_paired_sig)
```

##### Mummichog list
```{r}
yel_for_mummichog <- ctrl_noOutlier_t.test_paired %>%
  dplyr::select(mz_rt,
         p,
         statistic) %>%
  separate(col = mz_rt,
           into = c("m/z", "rt"),
           sep = "_") %>%
  rename("p-value" = "p") %>%
  rename("t-score" = "statistic")

write_csv(yel_for_mummichog,
          "yelcomp-mummichog-list-c18neg.csv")

```

#### Red
```{r}
# run paired t-tests for control intervention
red_t.test_paired <- df_for_stats %>%
  filter(Intervention == "Red") %>%
  dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ pre_post, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()
```

Statistically significant features
```{r}
# which features are significant?
red_t.test_paired_sig <- red_t.test_paired %>%
  filter(p <= 0.05)
kable(red_t.test_paired_sig)

# how many are significant?
nrow(red_t.test_paired_sig)
```


#### Red Without outlier
```{r}
# run paired t-tests for control intervention
red_noOutlier_t.test_paired <- df_for_stats_noOutlier %>%
  filter(Intervention == "Red") %>%
  dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ pre_post, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()
```

Statistically significant features
```{r}
# which features are significant?
red_noOutlier_t.test_paired_sig <- red_noOutlier_t.test_paired %>%
  filter(p <= 0.05)
kable(red_noOutlier_t.test_paired_sig)

# how many are significant?
nrow(red_noOutlier_t.test_paired_sig)
```


##### Mummichog export
```{r}
red_for_mummichog <- red_noOutlier_t.test_paired %>%
  dplyr::select(mz_rt,
         p,
         statistic) %>%
  separate(col = mz_rt,
           into = c("m/z", "rt"),
           sep = "_") %>%
  rename("p-value" = "p") %>%
  rename("t-score" = "statistic")

write_csv(red_for_mummichog,
          "redcomp-mummichog-list-c18neg.csv")

```

#### Post-red vs post-yellow

```{r}
# run paired t-tests for post interventions
post_t.test_paired <- df_for_stats %>%
  filter(pre_post == "post") %>%
  dplyr::select(Subject, Intervention, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ Intervention, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()
```

Statistically significant features
```{r}
# which features are significant?
post_t.test_paired_sig <- post_t.test_paired %>%
  filter(p <= 0.05)
kable(post_t.test_paired_sig)

# how many are significant?
nrow(post_t.test_paired_sig)
```

#### Post-red vs post-yellow Without Outlier

```{r}
# run paired t-tests for post interventions
post_noOutlier_t.test_paired <- df_for_stats %>%
  filter(pre_post == "post",
         Subject != "6106") %>%
  dplyr::select(Subject, Intervention, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ Intervention, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()
```

Statistically significant features
```{r}
# which features are significant?
post_noOutlier_t.test_paired_sig <- post_noOutlier_t.test_paired %>%
  filter(p <= 0.05)
kable(post_noOutlier_t.test_paired_sig)

# how many are significant?
nrow(post_noOutlier_t.test_paired_sig)
```

##### Mummichog export

```{r}
postcomp_for_mummichog <- post_noOutlier_t.test_paired %>%
  dplyr::select(mz_rt,
         p,
         statistic) %>%
  separate(col = mz_rt,
           into = c("m.z", "r.t"),
           sep = "_") %>%
  rename("p.value" = "p") %>%
  rename("t.score" = "statistic")

write_csv(postcomp_for_mummichog,
          "post-comp-mummichog-list-c18neg.csv")


```

#### Outlier comparison
Are there any significant features shared between tests with and without outlier?

```{r}
# return sig features present only in df with outlier, and not in df without outlier
kable(anti_join(post_noOutlier_t.test_paired_sig,
          post_t.test_paired_sig,
          by = "mz_rt"))

nrow(anti_join(post_noOutlier_t.test_paired_sig,
          post_t.test_paired_sig,
          by = "mz_rt"))

# return sig features from df with no outlier that are also present in df with outlier
kable(semi_join(post_noOutlier_t.test_paired_sig,
          post_t.test_paired_sig,
          by = "mz_rt"))

nrow(semi_join(post_noOutlier_t.test_paired_sig,
          post_t.test_paired_sig,
          by = "mz_rt"))
```


### Standard comparisons

Here, I want to only focus on the metabolites that I hypothesized to change. This way I can avoid multiple correction adjustments and see if I can capture any significant differences at different timepoints

#### Wrangle

```{r}
stds_df_for_stats_wide <- df_for_stats %>%
  dplyr::select(!rel_abund) %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2) %>%
  dplyr::select(c(1:11),
                "178.0515_3.479", #hippuric acid
                "179.0556_0.643", #glucose
                "154.0621_0.609", #histidine
                "191.0203_0.697", #citric acid
                "212.0027_3.304", #indoxyl sulfate
                "253.0503_5.048", #daidzein
                "257.0816_6.056" #O-DMA
                ) 

# make tidy df
stds_df_forstats_tidy <- stds_df_for_stats_wide %>%
  pivot_longer(cols = 12:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2")
```


```{r}
# changing factor levels for pre_post_intervention
stds_df_forstats_tidy$pre_post_intervention <- factor(stds_df_forstats_tidy$pre_post_intervention,
                              levels = c("pre_Yellow", "post_Yellow", "pre_Red", "post_Red"))

levels(stds_df_forstats_tidy$pre_post_intervention)  
```


#### Boxplots
```{r, fig.width=12}
stds_df_forstats_tidy %>% 
  ggplot(aes(x = pre_post_intervention, y = rel_abund_log2, fill = Intervention)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("Yellow" = "gold",
                                           "Red" = "tomato1")) +
  geom_line(aes(group = Subject), colour = "purple", linewidth = 0.2) +
  facet_wrap(vars(mz_rt), scales = "free_y") + 
  theme_bw() 
```

#### Control t-test
```{r}
# run paired t-tests for before vs. aftet control intervention
stds_ctrl_t.test_paired <- stds_df_forstats_tidy %>%
  filter(Intervention == "Yellow") %>%
  dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
  filter(Subject != 6106) %>% # remove outliers
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ pre_post, 
         paired = TRUE) %>% 
  add_significance()
```

Statistically significant features
```{r}
# which features are significant?
stds_ctrl_t.test_paired_sig <- stds_ctrl_t.test_paired %>%
  filter(p <= 0.05)
tibble(stds_ctrl_t.test_paired_sig)

# how many are significant?
nrow(stds_ctrl_t.test_paired_sig)
```

#### Red t-tests
```{r}
# run paired t-tests for before vs. after red intervention
stds_red_t.test_paired <- stds_df_forstats_tidy %>%
  filter(Intervention == "Red") %>%
  dplyr::select(Subject, pre_post, mz_rt, rel_abund_log2) %>%
  filter(Subject != 6106) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ pre_post, 
         paired = TRUE) %>% 
  add_significance()
```

Statistically significant features
```{r}
# which features are significant?
stds_red_t.test_paired_sig <- stds_red_t.test_paired %>%
  filter(p <= 0.05)
tibble(stds_red_t.test_paired_sig)

# how many are significant?
nrow(stds_red_t.test_paired_sig)
```

#### Intervention t-tests

```{r}
# run paired t-tests for post-red vs. post-control intervention
stds_post_t.test_paired <- stds_df_forstats_tidy %>%
  filter(pre_post == "post") %>%
  dplyr::select(Subject, pre_post_intervention, mz_rt, rel_abund_log2) %>%
  filter(Subject != 6106) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ pre_post_intervention, 
         paired = TRUE) %>% 
  add_significance()
```

Statistically significant features
```{r}
# which features are significant?
stds_post_t.test_paired_sig <- stds_post_t.test_paired %>%
  filter(p <= 0.05)
tibble(stds_post_t.test_paired_sig)

# how many are significant?
nrow(stds_post_t.test_paired_sig)
```

### Volcano plots

#### Post-intervention comparisons 

##### Wrangle (no outlier)
```{r}
# calculate mean rel abund (not log) by sample, and avg fold change (FC) diff by feature
red_v_yel_volcano_data_noOutlier <- df_for_stats %>%
  filter(pre_post == "post",
         Subject != 6106) %>% # remove outlier subj
  group_by(Intervention, mz_rt) %>%
  summarize(mean_rel_abund = mean(rel_abund)) %>%
  pivot_wider(names_from = Intervention, values_from = mean_rel_abund) %>%
  mutate(FC_postRed_div_postYellow = Red/Yellow) 

# bind back pval
red_v_yel_tobind_noOutlier <- post_noOutlier_t.test_paired %>%
  dplyr::select(p)

# calculate log2FC, and -log10p
red_v_yel_volcano_data_noOutlier <- 
  bind_cols(red_v_yel_volcano_data_noOutlier, red_v_yel_tobind_noOutlier) %>%
  mutate(log2_FC_postRed_div_postYellow = if_else(FC_postRed_div_postYellow > 0,
                                                  log2(FC_postRed_div_postYellow),
                                                  -(log2(abs(FC_postRed_div_postYellow)))), 
         neglog10p = -log10(p))


# create a df of features passing FC and pval cutoffs higher in post-Red
postRed_sig_red_v_yel_volcano_noOutlier <- red_v_yel_volcano_data_noOutlier %>%
  filter(p <= 0.05 & log2_FC_postRed_div_postYellow >= 0.6)

# create a df of features passing FC and pval cutoffs higher in post-control
postYellow_sig_red_v_yel_volcano_noOutlier <- red_v_yel_volcano_data_noOutlier %>%
  filter(p <= 0.05 & log2_FC_postRed_div_postYellow <= -0.6)  
```

##### Export sig features

Create feature lists with significant features along with their clusters
```{r}
#post-Red list
postRed_sig_intrvntn_comp_clusters <- left_join(postRed_sig_red_v_yel_volcano_noOutlier, cluster_features,
                                               by = "mz_rt")

write_csv(postRed_sig_intrvntn_comp_clusters,
          "Feature lists/postRed-sigfeatures-intervntn-comp.csv")

#post-Yellow list
postYellow_sig_intrvntn_comp_clusters <- left_join(postYellow_sig_red_v_yel_volcano_noOutlier, cluster_features,
                                               by = "mz_rt")

write_csv(postYellow_sig_intrvntn_comp_clusters,
          "Feature lists/postYellow-sigfeatures-intervntn-comp.csv")
```


##### Plot
```{r}
(red_v_yellow_volcano_noOutlier <- red_v_yel_volcano_data_noOutlier %>%
  ggplot(aes(x = log2_FC_postRed_div_postYellow, y = neglog10p, 
             text = glue("Mass_retention time: {mz_rt}
                         P-value: {p}
                         Fold change tomato/control: {round(FC_postRed_div_postYellow, 2)}"))) +
  geom_point(color = "grey") +
  geom_point(data = postRed_sig_intrvntn_comp_clusters, 
             aes(x = log2_FC_postRed_div_postYellow, y = neglog10p),
             color = "tomato") +
  geom_point(data = postYellow_sig_intrvntn_comp_clusters, 
             aes(x = log2_FC_postRed_div_postYellow, y = neglog10p),
             color = "yellow2") +
  geom_vline(xintercept = 0.6, linetype = "dashed", color = "grey") +
  geom_vline(xintercept = -0.6, linetype = "dashed", color = "grey") +
  geom_hline(yintercept = 1.3, linetype = "dashed", color = "grey") +
  coord_cartesian(xlim = c(-5, 8)) +
  labs(title = "Volcano Plot of Features Different in People After Tomato-Soy and Control Juice Consumption",
       subtitle = "Red points are higher after tomato-soy juice consumption while yellow points are higher \nafter control tomato juice consumption. Subject 6106 removed",
       caption = "Vertical dashed lines represent a log2 fold change > 0.6 or < -0.6, and horizontal dashed line represents an FDR corrected \np-value of 0.05.",
       x = "Log2 Fold Change (TomatoSoy/Control)",
       y = "-Log10(P-value)") +
  theme_bw() +
  theme(plot.title = element_text(size = 12, hjust = 0),
        plot.caption = element_text(hjust = 0.5)))

(red_v_yellow_volcano_ggplotly_noOutlier <- ggplotly(red_v_yellow_volcano_noOutlier, tooltip = "text"))
```

Save plots
```{r, eval=FALSE}
# save volcano plot
ggsave(plot = red_v_yellow_volcano_noOutlier,
       filename = "plots and figures/volcano plots/red_v_yellow_volcano_noOutlier.svg")

# save interactive volcano plot
saveWidget(widget = red_v_yellow_volcano_ggplotly_noOutlier,
           file = "plots and figures/volcano plots/interactive_redvyellow_volcano_plot_noOutlier.html")
```


#### Red

##### Wrangle (no outlier)
```{r}
# calculate mean rel abund (not log) by sample, and avg fold change (FC) diff by feature
red_volcano_data_noOutlier <- df_for_stats %>%
  filter(Intervention == "Red",
         Subject != 6106) %>%
  group_by(pre_post, mz_rt) %>%
  summarize(mean_rel_abund = mean(rel_abund)) %>%
  pivot_wider(names_from = pre_post, values_from = mean_rel_abund) %>%
  mutate(FC_post_div_pre = post/pre) 

# bind back pval
red_tobind_noOutlier <- red_noOutlier_t.test_paired %>%
  dplyr::select(p)

# calculate log2FC, and -log10p
red_volcano_data_noOutlier <- 
  bind_cols(red_volcano_data_noOutlier, red_tobind_noOutlier) %>%
  mutate(log2_FC_post_div_pre = log2(FC_post_div_pre),
         neglog10p = -log10(p))


# create a df of features passing FC and pval cutoffs higher in post-intervention compared to pre
red_pre_v_post_volcano_noOutlier <- red_volcano_data_noOutlier %>%
  filter(p <= 0.05 & abs(log2_FC_post_div_pre) >= 0.6)

```

##### Export sig features

Create feature lists with significant features along with their clusters
```{r}
red_sig_prepost_comp_clusters <- left_join(red_pre_v_post_volcano_noOutlier, cluster_features,
                                               by = "mz_rt")

write_csv(red_sig_prepost_comp_clusters,
          "Feature lists/Red-sigfeatures-PrevsPost-noOutliers.csv")

```


##### Plot
```{r}
(red_volcano_noOutlier <- red_volcano_data_noOutlier %>%
  ggplot(aes(x = log2_FC_post_div_pre, y = neglog10p, 
             text = glue("Mass_retention time: {mz_rt}
                         P-value: {p}
                         Fold change post/pre: {round(FC_post_div_pre, 2)}"))) +
  geom_point(color = "grey") +
  geom_point(data = red_sig_prepost_comp_clusters, 
             aes(x = log2_FC_post_div_pre, y = neglog10p),
             color = "tomato") +
  geom_vline(xintercept = 0.6, linetype = "dashed", color = "grey") +
  geom_vline(xintercept = -0.6, linetype = "dashed", color = "grey") + 
  geom_hline(yintercept = 1.3, linetype = "dashed", color = "grey") +
  coord_cartesian(xlim = c(-5, 8)) +
  labs(title = "Volcano Plot of Features Different in People After Tomato-Soy Juice Consumption",
       subtitle = "Red points are higher after tomato-soy juice consumption when compared to prior to consumption",
       caption = "Vertical dashed lines represent an abs(log fold change) > 0.6, and horizontal dashed line represents an FDR corrected \np-value of 0.05.",
       x = "Log2 Fold Change (Post/Pre)",
       y = "-Log10(P-value)") +
  theme_bw() +
  theme(plot.title = element_text(size = 12, hjust = 0),
        plot.caption = element_text(hjust = 0.5)))

(red_volcano_ggplotly_noOutlier <- ggplotly(red_volcano_noOutlier, tooltip = "text"))
```

Save plots
```{r, eval=FALSE}
# save volcano plot
ggsave(plot = red_volcano_noOutlier,
       filename = "plots and figures/volcano plots/red_volcano_noOutlier.svg")

# save interactive volcano plot
saveWidget(widget = red_volcano_ggplotly_noOutlier,
           file = "plots and figures/volcano plots/interactive_red_volcano_plot_noOutlier.html")
```

#### Yellow

##### Wrangle (no outlier)
```{r}
# calculate mean rel abund (not log) by sample, and avg fold change (FC) diff by feature
yel_volcano_data_noOutlier <- df_for_stats %>%
  filter(Intervention == "Yellow",
         Subject != 6106) %>%
  group_by(pre_post, mz_rt) %>%
  summarize(mean_rel_abund = mean(rel_abund)) %>%
  pivot_wider(names_from = pre_post, values_from = mean_rel_abund) %>%
  mutate(FC_post_div_pre = post/pre) 

# bind back pval
yel_tobind_noOutlier <- ctrl_noOutlier_t.test_paired %>%
  dplyr::select(p)

# calculate log2FC, and -log10p
yel_volcano_data_noOutlier <- 
  bind_cols(yel_volcano_data_noOutlier, yel_tobind_noOutlier) %>%
  mutate(log2_FC_post_div_pre = log2(FC_post_div_pre),
         neglog10p = -log10(p))


# create a df of features passing FC and pval cutoffs higher in post-intervention compared to pre
yel_pre_v_post_volcano_noOutlier <- yel_volcano_data_noOutlier %>%
  filter(p <= 0.05 & abs(log2_FC_post_div_pre) >= 0.6)

```

##### Export sig features

Create feature lists with significant features along with their clusters
```{r}
yel_sig_prepost_comp_clusters <- left_join(yel_pre_v_post_volcano_noOutlier, cluster_features,
                                               by = "mz_rt")

write_csv(yel_sig_prepost_comp_clusters,
          "Yellow-sigfeatures-PrevsPost-noOutliers.csv")

```

##### Plot
```{r}
(yel_volcano_noOutlier <- yel_volcano_data_noOutlier %>%
  ggplot(aes(x = log2_FC_post_div_pre, y = neglog10p, 
             text = glue("Mass_retention time: {mz_rt}
                         P-value: {p}
                         Fold change post/pre: {round(FC_post_div_pre, 2)}"))) +
  geom_point(color = "grey") +
  geom_point(data = yel_sig_prepost_comp_clusters, 
             aes(x = log2_FC_post_div_pre, y = neglog10p),
             color = "yellow2") +
  geom_vline(xintercept = 0.6, linetype = "dashed", color = "grey") +
  geom_vline(xintercept = -0.6, linetype = "dashed", color = "grey") +
  geom_hline(yintercept = 1.3, linetype = "dashed", color = "grey") +
  coord_cartesian(xlim = c(-5, 8)) +
  labs(title = "Volcano Plot of Features Different in People After Control, Yellow Tomato Juice Consumption",
       subtitle = "Yellow points are higher after control juice consumption when compared to prior to consumption.\nSubject 6106 removed",
       caption = "Vertical dashed lines represent abs(log2 fold change) > 0.6, and horizontal dashed line represents an FDR corrected \np-value of 0.05.",
       x = "Log2 Fold Change (Post/Pre)",
       y = "-Log10(P-value)") +
  theme_bw() +
  theme(plot.title = element_text(size = 12, hjust = 0),
        plot.caption = element_text(hjust = 0.5)))

(yel_volcano_ggplotly_noOutlier <- ggplotly(yel_volcano_noOutlier, tooltip = "text"))
```

Save plots
```{r, eval=FALSE}
# save volcano plot
ggsave(plot = yel_volcano_noOutlier,
       filename = "plots and figures/volcano plots/yel_volcano_noOutlier.svg")

# save interactive volcano plot
saveWidget(widget = yel_volcano_ggplotly_noOutlier,
           file = "plots and figures/volcano plots/interactive_yel_volcano_plot_noOutlier.html")
```


## Joined lists
Here, I want to venn significant features before I begin to look more into them. I am interested in the following effects: tomato effect, lycopene and/or soy isoflavones effect. 

### Tomato effect
1. Tomato effect: join a list that only keeps features that are both significant in pre vs. post-red and pre vs. post-yellow. Manually deleted the features that don't have the same directionality

```{r}
# keep only features present in both pre vs post red and pre vs post yellow
tomato_effect <- inner_join(red_sig_prepost_comp_clusters,
                            yel_sig_prepost_comp_clusters,
                            by = "mz_rt")
dim(tomato_effect)
```

Export venned list
```{r}
write_csv(tomato_effect,
          "Feature lists/sig-features-tomato-effect.csv")
```


#### Mummichog matches

```{r}
# read in hits from red 
red_mummichog_hits <- read_csv("../mummichog analysis/red-c18-mixed mode results-integ/red-c18-mummichog_matched_compound_all.csv") %>%
  mutate(Query.Mass = round(Query.Mass, digits = 4)) %>% # keep 4 decimal points for mass
  unite(mz_rt,
        c("Query.Mass", "Retention.Time"),
        sep = "_")

```


Mummichog makes a list of hits for all of the compounds, so we only need to do an inner join for one of the lists. The outcome would be the same for all of the lists used since I have to input my whole dataset (no cutoffs) into analysis. 

```{r}
mummichog_mass_matches_tomato <- inner_join(tomato_effect,
                                            red_mummichog_hits,
                                            by = "mz_rt")

length(unique(mummichog_mass_matches_tomato$mz_rt)) # how many?

tibble(unique(mummichog_mass_matches_tomato$mz_rt)) # which features have a hit with mummichog?

```





#### Boxplots


##### all sig tomato features

```{r}
# metabs with pval < 0.05 and fc >= 1.51
sigmetabs_tomato_effect <- tomato_effect$mz_rt 
```

```{r}
tomato_effect_df_for_stats_wide <- df_for_stats %>%
  dplyr::select(!rel_abund) %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2) %>%
  dplyr::select(c(1:11),
                (all_of(sigmetabs_tomato_effect)))

# make tidy df
tomato_effect_df_for_stats_tidy <- tomato_effect_df_for_stats_wide %>%
  pivot_longer(cols = 12:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2")
```

```{r}
# changing factor levels for pre_post_intervention
tomato_effect_df_for_stats_tidy$pre_post_intervention <- factor(tomato_effect_df_for_stats_tidy$pre_post_intervention,
                              levels = c("pre_Yellow", "post_Yellow", "pre_Red", "post_Red"))

levels(tomato_effect_df_for_stats_tidy$pre_post_intervention)  
```

```{r, fig.width=10}
tomato_effect_df_for_stats_tidy %>% 
  ggplot(aes(x = pre_post_intervention, y = rel_abund_log2, fill = Intervention)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("Yellow" = "gold",
                                           "Red" = "tomato1")) +
  geom_line(aes(group = Subject), fill = "brown", linewidth = 0.2) +
  facet_wrap(vars(mz_rt), scales = "free_y") + 
  theme_clean() 
```




### Lyc/soy effect
2. lycopene and/or soy isoflavones effect: join a list that only keeps features that are:
- significantly different between post-Red and post-Yellow, 
- and significant between pre- and post-Red. 
- remove features that are significant between pre-Yellow and post-Yellow.

```{r}
# combine sig features from post-red vs post-yellow
sig_postintervention_noOutlier <- full_join(postRed_sig_intrvntn_comp_clusters,
                                               postYellow_sig_intrvntn_comp_clusters)
dim(sig_postintervention_noOutlier)

# select only sig features that are present when comparing pre-Red to post-Red
lyc_soy_effect <- inner_join(sig_postintervention_noOutlier,
                             red_sig_prepost_comp_clusters,
                             by = "mz_rt") 
dim(lyc_soy_effect)

```

Export venned list
```{r}
write_csv(lyc_soy_effect,
          "Feature lists/sig-features-lyc-soy-effect.csv")
```

#### Mummichog matches

Mummichog makes a list of hits for all of the compounds, so we only need to do an inner join for one of the lists. The outcome would be the same for all of the lists used since I have to input my whole dataset (no cutoffs) into analysis. 

```{r}
mummichog_mass_matches_lycsoy <- inner_join(lyc_soy_effect,
                                            red_mummichog_hits,
                                            by = "mz_rt")

length(unique(mummichog_mass_matches_lycsoy$mz_rt)) # how many?

tibble(unique(mummichog_mass_matches_lycsoy$mz_rt)) # which features have a hit with mummichog?

```




```{r}
# map KEGG IDs to compound names
lycsoy_KEGGmatches <- as.data.frame(cpdkegg2name(mummichog_mass_matches_lycsoy$Matched.Compound))

mummichog_mass_matches_lycsoy$NAME <- lycsoy_KEGGmatches$NAME

```



#### Boxplots


##### all sig tomato features

```{r}
# metabs with pval < 0.05 and fc >= 1.51
sigmetabs_lycsoy_effect <- lyc_soy_effect$mz_rt 
```

```{r}
lycsoy_effect_df_for_stats_wide <- df_for_stats %>%
  dplyr::select(!rel_abund) %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2) %>%
  dplyr::select(c(1:11),
                all_of(sigmetabs_lycsoy_effect))

# make tidy df
lycsoy_effect_df_for_stats_tidy <- lycsoy_effect_df_for_stats_wide %>%
  pivot_longer(cols = 12:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2")
```

```{r}
# changing factor levels for pre_post_intervention
lycsoy_effect_df_for_stats_tidy$pre_post_intervention <- factor(lycsoy_effect_df_for_stats_tidy$pre_post_intervention,
                              levels = c("pre_Yellow", "post_Yellow", "pre_Red", "post_Red"))

levels(lycsoy_effect_df_for_stats_tidy$pre_post_intervention)  
```

```{r, fig.height=40, fig.width=30}
lycsoy_effect_df_for_stats_tidy %>% 
  ggplot(aes(x = pre_post_intervention, y = rel_abund_log2, fill = Intervention)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("Yellow" = "gold",
                                           "Red" = "tomato1")) +
  geom_line(aes(group = Subject), linewidth = 0.2) +
  facet_wrap(vars(mz_rt), scales = "free_y") + 
  theme_clean() 
```


### Low carotenoid tomato effect
2. yellow tomato effect: use list that only keeps features that are both significant between post-Red and post-Yellow timepoints, and significant between pre- and post-Yellow. These features are NOT significant between pre-Yellow and post-Yellow.

```{r}
# sig features from post-red vs post-yellow
dim(sig_postintervention_noOutlier)

# select only sig features that are present when comparing pre-Yellow to post-Yellow
low_carot_tomato_effect <- inner_join(sig_postintervention_noOutlier,
                             yel_sig_prepost_comp_clusters,
                             by = "mz_rt") 
dim(low_carot_tomato_effect)
```

Export venned list
```{r}
write_csv(low_carot_tomato_effect,
          "Feature lists/sig-features-low-carot-tomato-effect.csv")
```

#### Mummichog masses
```{r}
mummichog_mass_matches_yellow_tomato <- inner_join(low_carot_tomato_effect,
                                            red_mummichog_hits,
                                            by = "mz_rt")

tibble(unique(mummichog_mass_matches_yellow_tomato$mz_rt)) # which features have a hit with mummichog?
length(unique(mummichog_mass_matches_yellow_tomato$mz_rt)) # how many?
```


#### Boxplots


##### all sig yellow tom features

```{r}
# metabs with pval < 0.05 and fc >= 1.51
sigmetabs_yellow_effect <- as.character(low_carot_tomato_effect$mz_rt)
```

```{r}
yellow_effect_df_for_stats_wide <- df_for_stats %>%
  dplyr::select(!rel_abund) %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2) %>%
  dplyr::select(c(1:11),
                all_of(sigmetabs_yellow_effect))

# make tidy df
yellow_effect_df_for_stats_tidy <- yellow_effect_df_for_stats_wide %>%
  pivot_longer(cols = 12:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2")
```

```{r}
# changing factor levels for pre_post_intervention
yellow_effect_df_for_stats_tidy$pre_post_intervention <- factor(yellow_effect_df_for_stats_tidy$pre_post_intervention,
                              levels = c("pre_Yellow", "post_Yellow", "pre_Red", "post_Red"))

levels(yellow_effect_df_for_stats_tidy$pre_post_intervention)  
```

```{r, fig.width=12}
yellow_effect_df_for_stats_tidy %>% 
  ggplot(aes(x = pre_post_intervention, y = rel_abund_log2, fill = Intervention)) +
  geom_boxplot(outlier.shape = NA) +
  scale_fill_manual(values = c("Yellow" = "gold",
                                           "Red" = "tomato1")) +
  geom_line(aes(group = Subject), fill = "brown", linewidth = 0.2) +
  facet_wrap(vars(mz_rt), scales = "free_y") + 
  theme_clean() 
```

# Immuno Correlations

IL-5, IL-12p70, and GM-CSF were significantly decreased after tomato-soy intervention (only when comparing pre to post in Red) according to Wilcoxon rank-sum tests (p<0.05). Immune cell types were significantly altered in all 3 comparisons - so let's see how these significant immuno outcomes correlate with metabolites found to be significant at p > 0.05 and FC >=1.61.

Import other outcomes (carotenoids and immunology data)
```{r}
# load data
carot_immunology_meta <- read_excel("CompiledData_Results_Meta.xlsx",
                         sheet = "metadata_corrected_withsequence")

# clean up variable names 
carot_immunology_meta <- clean_names(carot_immunology_meta)
```

## Wrangle 

```{r}
# convert variables that should be factors to factors
carot_immunology_meta <- carot_immunology_meta %>%
  filter(intervention != "Baseline") %>%
  mutate(across(.cols = c("patient_id", "period", 
                          "intervention", "intervention_week", 
                          "pre_post", "sex", "sequence"),
                .fns = as.factor))


# some stuff came in as characters but should be numeric
carot_immunology_meta <- carot_immunology_meta %>%
  mutate(across(.cols = c("il_2", "il_10", "il_13", "il_4"),
                .fns = as.numeric))



# changing factor levels for pre_post
carot_immunology_meta$pre_post <- factor(carot_immunology_meta$pre_post,
                              levels = c("pre", "post"))

levels(carot_immunology_meta$pre_post)        

# Calculate total_cis_lyc, total_lyc, and total_carotenoids
carot_immunology_meta <- carot_immunology_meta %>%
  mutate(total_cis_lyc = other_cis_lyc + x5_cis_lyc,
         total_lyc = all_trans_lyc + total_cis_lyc,
         total_carotenoids = lutein + zeaxanthin + b_cryptoxanthin + 
                             a_carotene + b_carotene + total_lyc)
```


### Rm outliers

```{r}

# take outliers out of carot_immuno df 
carot_immunology_meta_noOutlier <- carot_immunology_meta %>%
  filter(patient_id != 6106)

# add sig immuno outcome columns to dataset
forcorr_metabslog2_immuno_noOutlier <- df_for_stats_wide_noOutlier %>%
  mutate(il_5 = carot_immunology_meta_noOutlier$il_5,
         il_12p70 = carot_immunology_meta_noOutlier$il_12p70,
         gm_csf = carot_immunology_meta_noOutlier$gm_csf,
         total_lyc = carot_immunology_meta_noOutlier$total_lyc,
         # sig immune cells pre vs. post red
         CD45ROpos_CD45RAneg_EMCD8 = carot_immunology_meta_noOutlier$x08_cd45ro_cd45ra_em_cd8,
         CD19posCD3neg_Bcells = carot_immunology_meta_noOutlier$x25_cd19_cd3_b_cells,
         CD27neg_IgDpos_naiveBcells = carot_immunology_meta_noOutlier$x26_cd27_ig_d_naive_b_cells,
         # sig immune cells post-yellow vs. post red
         CD16negNKcells = carot_immunology_meta_noOutlier$x38_cd16_nk_cells,
         CD14posMDSCs = carot_immunology_meta_noOutlier$x40_cd14_mdsc_mono)


```



Here, I am going to look at correlations between fold change differences and sig metabolites from the lycopene/soy effect list

### More wrangling
No outliers

Subset df's into pre and post 
```{r}
# pre
forcorr_pre_metabslog2_immuno_noOutlier <- forcorr_metabslog2_immuno_noOutlier %>%
  dplyr::select(Subject, Intervention, pre_post, 12:ncol(.)) %>%
  filter(pre_post == "pre")

# post 
forcorr_post_metabslog2_immuno_noOutlier <- forcorr_metabslog2_immuno_noOutlier %>%
  dplyr::select(Subject, Intervention, pre_post, 12:ncol(.)) %>%
  filter(pre_post == "post")
```

Create df with differences calculated for each outcome
```{r}
# difference for calculated for each outcome
forcorr_diff_red_metabslog2_immuno_noOutlier <- forcorr_post_metabslog2_immuno_noOutlier[,-c(1:3)] - forcorr_pre_metabslog2_immuno_noOutlier[,-c(1:3)]

```

## Lycsoy metab list Corr
These are correlations based on significant metabolites with FDR < 0.05 AND FC > 1.51 (from lyc/soy effect list) against immuno outcomes that were significant (padj < 0.05) when comparing pre to post red intervention.

```{r}
# add subject and intervention back to df. Select only significant metabolites (pval > 0.05 and FC >=1.51) that are in lyc/soy effect list
lyc_soy_forcorr_diff_red_metabslog2_immuno_noOutlier <- forcorr_diff_red_metabslog2_immuno_noOutlier %>%
  mutate(Subject = forcorr_post_metabslog2_immuno_noOutlier$Subject,
         Intervention = forcorr_post_metabslog2_immuno_noOutlier$Intervention) %>%
  filter(Intervention == "Red") %>%
  dplyr::select(Subject, Intervention, il_5, il_12p70, gm_csf, total_lyc, CD45ROpos_CD45RAneg_EMCD8, CD19posCD3neg_Bcells, CD27neg_IgDpos_naiveBcells, CD16negNKcells, CD14posMDSCs, all_of(lyc_soy_effect$mz_rt))

# correlation table
lycsoy_red_corr_results_metabs_immuno_diff_noOutlier <- lyc_soy_forcorr_diff_red_metabslog2_immuno_noOutlier %>% 
  correlate(method = "spearman") %>%
  rearrange() %>%
  shave()

kable(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier, format = "markdown", digits = 3)

```

Look for strongly correlated outcomes. R^2 >= to 0.5
```{r}

lycsoy_red_sigcorr_metabsP05_FC1.6_immunodata <- lycsoy_red_corr_results_metabs_immuno_diff_noOutlier %>%
  dplyr::select(term, il_5, il_12p70, gm_csf, total_lyc, CD45ROpos_CD45RAneg_EMCD8, CD19posCD3neg_Bcells, CD27neg_IgDpos_naiveBcells) %>%
  filter(abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$il_5) >= 0.5 |
           abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$il_12p70) >= 0.5 |
           abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$gm_csf) >= 0.5 |
           abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$total_lyc) >= 0.5 |
           abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$CD45ROpos_CD45RAneg_EMCD8) >= 0.5 |
           abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$CD19posCD3neg_Bcells) >= 0.5 |
           abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$CD27neg_IgDpos_naiveBcells) >= 0.5 |
           abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$CD16negNKcells) >= 0.5 |
           abs(lycsoy_red_corr_results_metabs_immuno_diff_noOutlier$CD14posMDSCs) >= 0.5) 

kable(lycsoy_red_sigcorr_metabsP05_FC1.6_immunodata, digits = 3, caption = "sig metabolites in lyc/soy effect list and sig immuno outcomes with strong correlations. R^2 >= to 0.5. Based on Pre vs. post-red log2fc")
```



### plot

```{r, fig.height=45, fig.width=40}
lycsoy_red_ggcorr_noOutlier <- round(cor(lyc_soy_forcorr_diff_red_metabslog2_immuno_noOutlier[,-c(1:2)], method = "spearman"), digits = 2)


(lyc_soy_immuno_metabs_ggcorrplot <- lycsoy_red_ggcorr_noOutlier %>%
  ggcorrplot(lab = TRUE, 
             outline.color = "white", type = "full", hc.order = FALSE,
             ggtheme = ggthemes::theme_clean(base_size = 8, base_family = "sans"),
  colors = c("#6D9EC1", "white", "#E46726")) +
  theme(axis.text.x = element_text(angle = 90, size = 12),
        axis.text.y = element_text(size = 12)))
```

Export corplot
```{r, eval=FALSE}
ggsave(plot = lyc_soy_immuno_metabs_ggcorrplot,
       filename = "plots and figures/prepostRedFC-C18-Neg-lycsoy-Metabs-and-immunology-corrplot.svg",
       width = 45,
       height = 40,
       scale = 1)
```


export a df for isoflavones analysis
```{r}
write_csv(imp_metabind_clust_log2_noQCs, file = "meta_omics_noQCs.csv")
```

